Exploring MagicSchool.ai to Help Teach Musical Styles

Author’s Note: This post takes a practical approach to using MagicSchool’s generative AI functions. The many ethical issues that surround the use of AI generative tools have been published broadly. The Center for Teaching Innovation at Cornell University presents an outline of some ethical concerns that can inform educators as they navigate these complexities and make decisions for their own practice. 

Just about a year ago, author/educator Chris Mah posted a helpful article How to Use ChatGPT as an Example Machine on the Cult of Pedagogy blog. AI use in education has inarguably sprung forward exponentially since then, but most discussions continue to center around using generative AI as a writing tool, and far fewer talk about its value as a thought partner. However, when I work with authors of online courses, I frequently suggest that they explore AI as a thought partner. 

But how? Well, as Mah writes, “ChatGPT’s ability to quickly produce a lot of examples of different kinds of texts makes it uniquely suited to learning strategies like concept attainment, inductive learning, or one that we’ll focus on here called “contrasting cases.” 

I did some work of my own, in exploring the use of AI in creating contrasting examples as a way to teach musical styles. In this post, I have extended Mah’s work into the musical performance realm, specifically exploring its ability to help teachers define differences between musical styles using the tools built into MagicSchool. (NOTE: MagicSchool is an aggregator; it does not specifically indicate which AI tools it uses for its various functions, so the focus here is on the results, not an evaluation of the underlying generative AI LLM model that created them.)

Why Contrasting Examples Support Learning

Contrasting examples as a strategy is built on the premise that one example of an idea or concept is generally not enough to help people truly understand its complexities. Contrasting cases help us fill in the blanks and help learners identify the subtleties of a thing’s “thingness.” “Not coincidentally, ChatGPT is built on a similar type of machine learning,” Mah writes. He notes that for a computer to recognize an image of a dog, it has to process thousands of images of dogs alongside images of “not dogs,” like cats, foxes, and wolves. “In other words,” he writes, “to know what a dog is, you also need to know what it is not.” 

Agreed: It helps to understand what a thing is not in order to better understand what it is. Mah points out, however, that coming up with appropriate contrasting examples takes time, focus, and energy—all of which are precious and rare in an educator’s schedule. Mah points out, however, that since ChatGPT’s thinking engine is in part designed on determining “is-ness” by eliminating “is-not-ness,”  it can be a particularly intrepid rescuer to educators stranded in the wilderness of “Finding Contrasting Examples (Also I’m Super Busy and Perhaps A Bit Overwhelmed).” 

Here, an AI-based tools like MagicSchool can indeed be our hero/ine’s valiant rescuer, but like the confidant/foil character in any great story, it is also inherently flawed—so careful prompting and iteration is in order when trying to wrestle good contrasting examples from the steely grasp of any generative AI tool. As such, Mah designed a five-step process for getting the most out of using a tool like ChatGPT as an “example machine”:

  1. Establish criteria: Identify the elements that define a strong example of the thing you want learners to know or be able to do.
  2. Prompt: Prompt ChatGPT to create an example of the concept, process, or phenomena you are trying to teach.
  3. Review and adapt: Review the bot’s output and adapt it as you see fit.
  4. Prompt for contrasting cases, such as non-examples, incorrect examples, or ambiguous examples.
  5. Review and adapt. 

So with the backstory told, finally, on page 2, our adventure begins: What happens if we apply Mah’s five-step method to teaching musical styles through contrasting examples? 

(The final result appears at the end of this post.) 

Step 1. Establish criteria.

Explain to a new author of a continuing education online course for young teens or adults about why it is beneficial to the learning process to have students listen to contrasting musical examples in order to understand a specific style of instrumental music performance, such as jazz, pop, or any folk music tradition that uses that instrument.

First, we convince. Above is how I prompted MagicSchool to explain why listening to contrasting examples of musical styles is important. 

Output, Prompt 1.

The result here didn’t show any nuanced understanding of musical performance, and I also felt like it would work better if I focused on a particular instrument, to see if it gave more instrumental specifics. Second, the examples were far too broad and seemed unhelpful. Somewhat poor examples undercut the value of the good parts of its output. Next time, I wanted to narrow to comparing two styles only. I also would like to know if there are any theories of musical learning that would support it—or, if not, theories of learning that apply well to music learning—and if so, include those in the explanation, and do not list more than three theories. 

Steps 2 and 3. Create the Prompt, Review, Adapt.

Prompt 2: 

Explain to a new author of a continuing education online course on violin performance for young teens or adults about why it is beneficial to the learning process to have students listen to contrasting musical examples in order to understand a specific style of violin performance, such as comparing Irish and Scottish folk fiddle music. Include in your reasoning at least two, but no more than three, examples of theories of musical learning that support the use of contrasting examples. If there is no specific musical learning theory, then please instead include two to three theories of learning that apply well to music learning.

Output, Prompt 2.

Step 4. Prompt for Contrasting Cases, Refine, Adapt

The results were again… lousy… and showed no nuanced understanding of either style. It relies on general and somewhat clichéd perspectives on what each style is like. Irish music is “lively”—but not all Irish music is such, and this is a very cliched way to describe the music. I need it to refer to more specific instrumental techniques. The learning theories it cites—Constructivist Learning Theory and Experiential Learning Theory—do seem apt. I wonder if there are specific music learning theories that are not written about? Is there such a thing?

Prompt 3: 

Explain to a new author of a continuing education online course on violin performance for young teens or adults about why it is beneficial to the learning process to have students listen to contrasting musical examples in order to understand a specific style of violin performance, such as comparing Irish and Scottish folk fiddle music. Include in your reasoning at least two, but no more than three, examples of theories of musical learning that support the use of contrasting examples. If there is no specific musical learning theory, then please instead include two to three theories of learning that apply well to music learning. When you give examples of contrasting musical styles at the end, do not refer to Irish music as “lively” (because it is NOT always lively) and do not refer to Scottish music in general terms, such as just “bowing,” but rather, instead explain the musics in terms of specific and technical execution, covering what a fiddler actually DOES to make the characteristic sounds. Avoid cliche, please, and use real musical instruction. 

Output, Prompt 3.

The results were a little better, but still too general and lacking complete knowledge of the specific musical techniques involved in achieving a particular sound, tone, and style. Also, a problem: When it came to learning theories, it completely replaced the two learning theories with two different  theories, so in subsequent prompts, I will have to specifically ask it to only use the first two.  

Step 5. Review and Adapt, Ad Nauseum

Prompt 4:

Can you regenerate that last prompt, only adding two more learning theories, which are: Constructivist and Experiential?

And guess what it did? Generated a handful of sentences and stopped midstream for no clear reason. Try again, with a more specific prompt. 

Output, Prompt 4.

Prompt 5. 

I am going to paste in an entire piece, and I would like you to also add two more theories to the Supporting Learning Theories section: Constructivist and Experiential. Nothing else should change. Let me know when you’re ready.

Output, Prompt 5.

At this point, I’ve given up on getting a more nuanced understanding from AI. I would need to use specific elements from my own knowledge to construct the details I want, and I would need to set up criteria for what students should listen to in their activity. One next step might be to have MagicSchool generate the activity based on criteria and learning theories I’ve pulled so far. Time for my own additions, I think. Contrasting examples will rely on much more specific subject area knowledge. In an oral tradition, less is physically written about it so AI has limited resources—people teach music, especially aurally/orally transmitted folk styles—through demonstration, imitation, and repetition—not through verbal or written description. 

Prompt 6:

Based on all that you have generated so far, can you give me specific instructions for a learning activity for students in which they listen to an Irish reel and a Scottish reel and identify what is different in the two playing styles? Give them criteria for what to listen for. The activity should be based on Constructivist and Experiential Learning theories, but do not mention either theory. This is to be written for a student at the 8th grade level, who has a minimum of two years playing experience. Keep your text limited to 400 words. Please also suggest specific recordings to listen to in the activity. 

Output, Prompt 6.

Not bad, but I fear the differences are too subtle—and I specifically said “two reels” and it gave me a jig and a reel. Second, the performers—not sure the Chieftains are a good example. It should be solo fiddler, and I would choose a player who is more of a torch bearer. They are more liberal with their interpretations. I’m also realizing that even comparing two folks styles is challenging, and the best approach is to sort of find a “control group”—i.e., “no style at all.” So I chose Classical, and ditched Scottish, as it might be easier to compare folk to not-folk, rather than two subtle variations between folk styles that are rather close. 

Prompt 7:

Now suggest the same activity for a piece in 6/8 in the Classical style, versus an Irish jig.

Output, Prompt 7.

And… at this point, I could possibly be swearing out loud (if I were that type of person) because it keeps selecting one of the most cliched tunes in Irish music, which in this case is NOT a jig in 6/8. It is a slip jig in 9/8. I am going to try one more time, this time giving more specifics on what classical piece (it has to be a violin solo part) and also give a specific jig mentioned in the class. Second, the piece in a classical style was an orchestral piece that I am not familiar with, but I am sure that the violin sections are not solo. It would be important to evaluate solo style playing to better compare apples to apples. 

Step 6. (My Addition): Move on.

Prompt 8.

Can you update this same activity, but this time focus on Morrison’s Jig for the Irish tune, as played by a solo fiddler, and then for the Classical Piece, choose one that has a section that is solo or soli violin part with a melody that is in 6/8 and is all eighth notes.

Output, Prompt 8.

And here is where I called uncle. I recognized I needed more content-area knowledge of Classical music to ensure that the Classical piece is a good example. Then, I need to find specific recordings, and then adapt the activity. The result might work as a skeleton, but I would need to adapt it based on deep content area knowledge, and improve the writing to be less boring and pedestrian. I don’t expect that further refinements will take me much closer than I am, because it appears that the resources are simply not there in the source database.  

FINAL RESULT and Reflection:

This is what AI gave me after eight  prompts and much iteration. My conclusion is that using MagicSchool.ai to help with designing learning activities can be helpful, but there is still much to do to make the content usable. Not a huge fan, yet. There are many reasons why. The two primary are: 1) the language is dry, cliché, and boring (i.e., totally unengaging), and 2) the content does not exhibit a nuanced “understanding” of the musical styles it mentions.

The real problem is that we do not learn musical styles by describing their technical elements in words alone. We listen. We immerse ourselves in the music and the culture. We imitate. We repeat. Creating a language-based solution to a problem that is not language based is not the best idea, but it took me a great deal of iteration to recognize this.

This description below uses terms that sound good but that are too ambiguous, broad, and …well… incorrectly used for any real practical application.

The Benefits of Listening to Contrasting Musical Examples in Violin Performance

In a course focused on violin performance, particularly when examining styles like Irish and Scottish folk fiddle music, encouraging students to listen to contrasting musical examples is crucial for their development. This approach not only enhances their understanding of specific stylistic elements but also deepens their technical skill set.

Developing aural skills through differentiation: By listening to contrasting examples, students learn to identify and appreciate the subtle differences in technique and expression between styles. For instance, an Irish fiddler often employs a technique known as cutting, where a quick grace note is played before the main note to add ornamentation. In contrast, a Scottish fiddler may use doubling, which involves playing two notes simultaneously (often a note and its octave) to create a rich sound. These contrasting approaches not only highlight the distinct characteristics of each style but also encourage students to refine their own technical execution.

Supporting Learning Theories: Several learning theories reinforce the value of contrasting musical examples:

  • Cognitive Learning Theory: This theory suggests that learners construct knowledge by processing information. When students listen to contrasting fiddling styles, they actively engage in recognizing patterns and techniques. For example, listening to a traditional Irish tune like “The Kesh Jig,” where the fiddler uses triplet bowing to create a fluid rhythm, compared to a Scottish tune like “The Atholl Highlanders,” where the fiddler might emphasize swinging eighth notes and drones, helps students cognitively map out what they hear in relation to what they play.
  • Social Learning Theory: This theory emphasizes learning through observation and imitation. By listening to recordings of skilled fiddlers from both traditions, students can model their playing after these examples. For instance, observing how a Scottish fiddler employs cross-string bowing techniques to create a smooth legato sound can inform their practice, allowing them to emulate these techniques in their own playing.
  • [There were two more, but I deleted due to assignment guidelines]

Enhancing Technical Execution: Listening to contrasting musical examples not only develops listening skills but also informs students about the specific technical execution required in each style. For instance, in Scottish fiddle music, the use of double stops (playing two strings simultaneously) is common and contributes to the music’s harmony and depth. On the other hand, Irish fiddle music might focus more on ornamentation and fast runs that require precise finger placement and bow control.

Examples of Contrasting Musical Styles:

  • Irish Fiddle Music: In the piece “The Butterfly,” a fiddler might showcase rapid ornamental techniques such as rolls and cuts, emphasizing quick transitions between notes that create a flowing sound.
  • Scottish Fiddle Music: In contrast, a piece like “The Drunken Sailor” may feature syncopated rhythms and grace notes, along with bowing techniques like slurs that connect multiple notes smoothly, emphasizing the melodic contour of the music.

By integrating contrasting musical examples into their learning process, students gain a nuanced understanding of violin performance across styles, enriching their technical skills and overall musicianship. Scottish and Irish music may be too close for real contrast, so I would recommend a more stark contrast, such as Classical and Irish traditional folk. The results are below, which still need a great deal of tweaking by a subject matter expert.

Activity: Exploring Morrison’s Jig and a Classical Piece with a Solo Violin in 6/8

Objective: In this activity, you will listen to and analyze “Morrison’s Jig,” a traditional Irish tune, alongside a classical piece featuring a solo violin section in 6/8 time. You will identify the differences in playing styles and techniques, enhancing your understanding of these musical forms.

Materials Needed:

  • Access to audio recordings
  • Notebook and pen for notes

Recordings to Listen To:

  1. Irish Jig: “Morrison’s Jig” performed by a solo fiddler (search for recordings by artists like Kevin Burke or Liz Carroll).
  2. Classical Piece: “Zigeunerweisen” by Pablo de Sarasate (look for a recording that highlights the solo violin part).

Instructions:

  1. Preparation:
    • Find a quiet space where you can listen to the recordings without distractions.
    • Have your notebook ready to jot down your observations.
  2. Listening Session:
    • Play the first recording of “Morrison’s Jig.” Listen carefully to how the fiddler plays the notes.
    • After listening, pause and write down your initial thoughts. Focus on the following criteria:
      • Rhythm: What kind of rhythm do you hear? Is it lively and bouncy, or does it have a different feel?
      • Ornamentation: Are there any special techniques like cuts, rolls, or grace notes? How do they contribute to the overall sound?
      • Tempo: Is the tempo fast and energetic? How does it affect the feel of the music?
      • Expression: How does the fiddler convey emotion through their playing?
  3. Repeat:
    • Now, listen to the classical piece “Zigeunerweisen.” As you listen, pay attention to the same criteria you used for “Morrison’s Jig.”
    • Again, pause after the recording and write down your observations.
  4. Comparison:
    • Once you have noted down your thoughts for both recordings, compare your findings. Use the following questions to guide your analysis:
      • How do the rhythms differ between the two pieces?
      • What types of ornamentation are used in each style? Which style uses more embellishments?
      • How does the tempo contribute to the character of each piece?
      • What emotional responses do you feel from each piece? How does the performer’s style influence this?
  5. Discussion:
    • Share your observations with a classmate or in a small group. Discuss what you discovered about the differences in the Irish jig and the classical solo violin piece and how these differences shape your understanding of each tradition.

By engaging in this activity, you will deepen your appreciation for the nuances of “Morrison’s Jig” and the classical violin repertoire, while practicing your listening and analytical skills.

The Big Question:

On Adolescent Creativity in Online Learning

The final assignment for EDU800 was an “elevator pitch”—an exercise in developing the ability to clearly and succinctly communicate our current interests at any given time. Wow. It was hard—not the writing of it; the delivering of it. We were asked to create and post a two-minute video outlining our “big question” and our research interests. Below is take 572,846,000. This was tricky!

A second note: As an exploration, I used ChatGPT to cut down my original reflection from 750 words to 350. It did a great job of keeping the message, but not so great a job at keeping my “voice.”

Here’s the full version of where I hope to be going with my research… and why.

Opportunities for self-driven, informal learning for teens are on the rise, but little is known about what motivates students to seek these activities and what keeps adolescents engaged when they find these opportunities. There also is plenty of research on higher education online learning, but only scant research specifically on adolescent creativity in online courses. This makes it challenging to address a question I face every day in my work:

What instructional elements and techniques will best inspire motivation and engagement in teens in online courses in the creative arts?

Identifying the answer to this question requires examining key aspects of teen learning: motivation, engagement, creativity scholarship, the teen brain, and what research has revealed about features that engage teens in online courses.

I draw inspiration from established theories such as Self-Determination Theory, Garrison’s Community of Inquiry engagement, the ARCS model of instructional design motivation, and the Adolescent Community of Engagement framework. I will also look at recent scholarship on creativity, emphasizing both everyday creativity (small-c) and transformative creativity leading to significant breakthroughs or masterpieces (Big-C).

Understanding the nature of creativity involves delving into the sociocultural perspective of creativity, exemplified by scholars like Glăveanu. His work underscores the role of creativity in societal betterment. This vision of creativity aligns with the mission of the Interlochen Center for the Arts, which advocates for art as a transformative force. The institution’s commitment to purpose-driven creativity distinguishes it from other artistic enterprises, and reflects the belief that purpose underlies all activities of a Big-C creative individual.

So how can purposeful creativity be encouraged, motivated, and nurtured online?

To answer this question, I will look at what drives teens in the creative arts and what they report makes them feel engaged in online learning. I’ll also seek to identify effective strategies for teachers to nurture creativity in online learning environments, devise optimal ways for parents to support their teens in online learning, and explore existing models to determine which are most adaptable to online arts education.

Drawing from research in motivation, online learning, adolescent learning, creativity, and instructional design, the goal is to formulate the optimal model for online arts education that not only provides the opportunity for students to hone their artistic skills but also empowers them to leverage their creativity purposefully for the betterment of society.

The Adolescent Community of Engagement: A Framework for Research on Adolescent Online Learning

Image: Joy Reeves – The Center for Transformative Teaching and Learning at St. Andrew’s Episcopal School.

ANNOTATION: Borup, J., West R., Graham C. and Davies, R. (2014) The Adolescent Community of Engagement: A Framework for Research on Adolescent Online Learning. Journal of Technology and Teacher Engagement 22(1), 107-129.

Enrollments in K-12 online learning continue to increase at a rapid pace, despite findings that suggest far higher attrition rates among younger students than among university and adult students. Numerous frameworks have been developed to describe and understand higher education online learning, but none of these are sufficient to address the specific elements that would ensure success of adolescent students in an online learning environment. This paper presents a framework adolescent online learning, called the Adolescent Community of Engagement (ACE).

The ACE framework is built on a review of literature. It is based on existing higher ed frameworks, building from three constructs of engagement in higher education online learning: student to teacher, student to content, and student to student. However, the ACE relies on on research from in-person adolescent learning to add an additional construct: student to parent engagement.

The authors of this paper review three prevalent theoretical frameworks on online learning, then evaluate existing frameworks on parent involvement, and apply these to the online environment. The first of these comes from Moore (1989, 1990), who first defined the primary three types of learner interaction in educational contexts. 1) Learner-content refers is student use of materials. 2) Learner-instructor interaction builds on the materials and provides motivation and feedback. 3) Moore posited that learner-learner interaction may be more significant for adolescents, and according to several citations within this article, adolescent learners are also affected by their families, workplaces, and communities.

A second contributing framework is Garrison et al.’s Community of Inquiry, which emphasizes communication to minimize the transactional distance, or “remoteness,” of online learning. Garrison identifies the critical importance of teaching presence, social presence, and cognitive presence, but designates teaching presence as the foundation for all of them.

Borup et al review several examples of research on the role of parent engagement in improving outcomes in face-to-face learning. Parents help students organize their time, stay on top of deadlines, access the content, and also model desired academic behaviors, particularly in the way they place value upon study and learning by communicating with teachers, acting on recommendations, and taking part in school activities to demonstrate the importance of school in their children’s lives.

The researchers’ primary hypothesis is that as parent, teacher, and peer engagement increase, student engagement will likely increase too.

Regarding each type of engagement, the authors conclude:

  • Student Engagement: Student engagement is the primary focus of the ACE framework, with subtypes defined as emotional, behavioral, and cognitive engagements.
  • Teacher Engagement: This refers to the teacher’s ability to impact engagement through 1) facilitating interaction, 2) organizing course materials and timelines, and 3) directly instructing students. Teacher responsibilities extend beyond discussion boards, facilitating interaction, and assessments to also include emotional support, nurturing, motivating, and monitoring, and providing guidance on organization and study skills.
  • Parent Engagement: This can include facilitating interaction, helping students organize their environments, and instructing students directly. There are many overlaps with parent and teacher engagement, and extant research indicates that greater outcomes can be achieved when they work in concert. In addition to performing teacherlike duties, parents can also help improve their childrens’ learning outcomes by volunteering at the school or participating in other school activities to help reinforce the importance of the school experience in their childrens’ lives.
  • Peer Engagement: Research indicates that students can help each other learn via mutual instruction and project collaboration. Still, adolescent students have less life experience than adults may need more teacher scaffolding to help them construct knowledge in collaborative contexts. Students also can help motivate fellow students.

A second fundamental hypothesis of this literature review is that all of the attempts of parents, teachers, and peers can be enhanced with modeling and social presence. A key implication is that teachers’ engagement can help encourage student engagement because it models what academic engagement online should look like, citing Bandura (1986) in asserting: “…behavior is more likely to be emulated when modeled by someone with high status.”

ADDITIONAL READINGS REVIEWED IN WEEK 15:

Caskurlu, S., Maeda, Y., Richardson, J., and Lv, J. (2020) A meta-analysis addressing the relationship between teaching presence and students’ satisfaction and learning. Computers and Education 157, https://doi.org/10.1016/j.compedu.2020.103966.

Petrie J. A. C. van der Zandena, *, Paulien C. Meijera , Ronald A. Beghettob (2020) A review study about creativity in adolescence: Where is the social context? Thinking Skills and Creativity, 38: 100702. https://doi.org/10.1016/j.tsc.2020.100702.

Kolyvas, S. and Nikiforos, S. (2021). Technology and Creativity on early adolescence: A case study during COVID-19 pandemic. Current Psychology 42(10): 8554-8561. DOI: 10.1007/s12144-021-02349-4

Chowkase, A. (2022). Online learning, classroom quality, and student motivation: Perspectives from students, teachers, parents, and program staff. Gifted Education International 38(1): 74-94. DOI: 10.1177/02614294211060401.

Wang, Li. (2022) Student Intrinsic Motivation for Online Creative Idea Generation: Mediating Effects of Student Online Learning Engagement and Moderating Effects of Teacher Support. Frontiers in Psychology 13, DOI: 10.3389/fpsyg.2022.954216.

Higher Order Thinking in an Online World: Toward a Theory of Web-Mediated Knowledge Synthesis

ANNOTATION: Higher Order Thinking in an Online World: Toward a Theory of Web-Mediated Knowledge Synthesis

Deschryver, M. (2014). Higher order thinking in an online world: Toward a theory of web-mediated knowledge synthesis. Teachers College Record, 116(12), 1-44.

Access to the web is nearly ubiquitous at home, work, and school. Most people use the web for straightforward information-based tasks, but its capacity for inspiring higher-order thinking and learning is potentially transformational. (The author cites multiple resources on this assertion.) The problems humans face are increasingly complex and emerge at an increasingly rapid rate, and the web has not been fully exploited for its ability to inform decision-making and creative solutions to difficult problems. Emancipated from the need to memorize and store information, humans can now devote more of their intellectual power to engage instead in higher-order thinking, such as creativity, analysis, and integration of ideas (cited by the author, Anderson & Raine, 2010).

At the time of the writing of this paper, the author asserted that education rarely addresses these kinds of uses of the web, and pointed out that a new framework is called for to help frame approaches to understanding educative processes that the web can enable. “The ability to create new knowledge, to synthesize meaning that is neither explicit nor implicit [in web resources] will move students away from simple information consumption and toward more complex knowledge generation” (Deschryver, 2014, p. 4). In this paper, Deschryver proposes a literature-reinforced, multipronged theory to help researchers understand, describe, and evaluate how advanced students generate new ideas from web-based resources.   

Deschryver bases his theory on a “purposeful sampling” empirical study whose theoretical framework draws from literature in a several domains of education: educational psychology, reading comprehension, hypertext and web-based reading, cognitive flexibility, and creativity. It defined higher-order thinking with the Bloom’s term “synthesis,” and further subcategorized the synthesis process as: synthesis for meaning (understanding and deducing), generative synthesis (insights and lightbulbs!), and creative generative synthesis (creating new products based on these insights and knowledge). The dual research goals were to test whether three forms of synthesis existed in a web-based environment and then describe them, and to capture any new forms of synthesis that hadn’t been accounted for in the prior literature review.

Eight doctoral and law students were chosen for a think-aloud project that involved 1) reading to learn, and 2) reading to do. They were given a finite amount of time to use the web to research, understand, and then propose a novel solution for a complex problem in a knowledge are of which they did not have extensive expertise. The subjects used a browser, practiced a think-aloud to detail their cognitive processes, and used their own online and offline process to store and organize their thoughts and the information. Additionally, screen recordings captured online behaviors, post-interviews gathered additional insights, and clickstreams and notes helped to further elucidate each person’s process. Data were coded and analyzed.

The resulting analysis revealed that effective web-mediated knowledge synthesis in this population relied on seven primary activities:

  1. Divergent keyword search phrases: Subjects used a variety of novel and/or unique search terms.  
  2. Synthesis for meaning: subjects made extensive use of organizing, combining, composing, rearranging, rewriting, compiling and structuring text from various sources to derive meaning from multitudes.
  3. In the moment insights: Light bulbs lit from single or multiple resources, activity unconnected to the web, or a combination of these.
  4. Repurposing: Participants applied knowledge to understand different phenomena and other contexts.
  5. Reinforcement: Ideas were reinforced for students who encountered information in new contexts that they had already encountered in prior ones.
  6. Note-taking: Online and offline note-taking aided students’ comprehension and synthesis.  
  7. Creative synthesis: Subjects demonstrated at least one example of an ability to combine disparate ideas, concepts or perspectives to generate new/innovative products or solutions.

The author indicated some limitations noted for further research. One is the think-aloud process yields only as much information as an individual is able to express and recognize about their own process. The theory is descriptive but not predictive, in that it borrowed from the field of creativity research to craft a more precise description of how individuals construct meaning and new ideas from multiple sources they read on the web.  The work highlighted the importance of note-taking to higher-order thinking and learning via the web. Finally, it presents an understanding that takes into account the many factors and areas of knowledge that influence how individuals synthesize existing information to make new understanding.

An important contribution to this work is that its insights will be helpful to those who are engaged in moving education away from a focus on rote learning toward the twenty-first-century approach that emphasizes creative problem solving, adaptability, flexibility, and cognitive agility—a “new ecology of thinking,” because as Deschryver aptly states, in the web-mediated world,  “Just because learners have more time to engage in higher order thinking does not mean they know how to do it.” This work is designed to provide a foundation.

 Creating technology-enhanced, learner-centered classrooms: K–12 teachers’ beliefs, perceptions, barriers, and support needs

PHOTO: iStock

ANNOTATION: An, Y. J., & Reigeluth, C. (2011). Creating technology-enhanced, learner-centered classrooms: K–12 teachers’ beliefs, perceptions, barriers, and support needs. Journal of Digital Learning in Teacher Education, 28(2), 54-62.

Twenty-first century educational thinking moves beyond what Reigeluth (cited in paper, 1994, 1999b) referred to a factory-model “sorting” role, to achieve a higher-order learning role better designed to help students meet information-age imperatives, including flexibility, creativity, relevance, diversity, and critical thinking—values and skills critical to thriving (or at least surviving) the rapidly-changing needs of the information age.

The learner-centered model of education prioritizes the development of real-world skills, including collaboration, critical thinking, and problem solving, with the underlying goal of motivating and deepening learning through factors such as personalized and customized learning, autonomy, a supportive social and emotional environment, collaborative and authentic experiences, and adequate technology integration that enables each of these elements.

Technology has been increasingly included as a motivational and engagement device in schools, but a major problem that was yet unsolved as of this writing (and perhaps still unsolved, more than twelve years later), is that teachers are not using technology as effectively as they could be to promote deep learning. They typically use technology for low-level activities like communication, word processing, and drill and practice activities. The challenge is in moving faculty beyond just knowing the technology, into knowing how to leverage it for student self-guided knowledge construction.

In order to help enable that goal, the researchers/authors of this paper sought to determine teachers’ perceptions, beliefs, barriers, and support needs regarding the use of technology in learner-centered classrooms. Data was gathered from online Likert-style surveys issued to 126 teachers representing a range of SES and grade levels. The survey queried teachers on demographics, technology beliefs, learner-centered instruction, current practices, perceived barriers, perceptions of current professional development, and other support needs. Some of the key findings among this population of teachers were:

  • Teachers believe technology helps students learn and accomplish tasks more efficiently.
  • Teachers have overall positive feelings about classroom technology (70% positive).
  • Teachers believe they are providing positive emotional support to their students, and are teaching for diverse needs.
  • Lack of time and money were primary barriers to technology adoption–even greater than lack of knowledge.
  • PD sessions should emphasize depth not breadth, and offer ample hands-on training—“show not tell.”
  • Less than half of teachers were satisfied with their districts’ professional development, and most wished for more specific training in how to apply technology in the classroom, rather than to simply “use” technology.
  • Teachers are frustrated with school focus on standardized test scores.

Further research with a broader population of teachers beyond just teachers in the Tex/Arkana region of the US could help determine the level of generalizability of these results, though I do find that these attitudes are more or less aligned with what I know from my own experience as a public school teacher.

The biggest takeaway is that teachers crave hands-on techniques and practical ideas, not instruction-free tools and big-mind philosophies. This research suggests an air of openness to new approaches coupled with a hunger for concrete applications. They are willing to help students construct the kind of self-direction that will ensure their success in a rapidly changing world—but they want more specific guidance on how to do so.

On the Nature of Creativity: Drawing from the PISA 2021 Creative Thinking Framework

CITATION: OECD. (2019). Framework for the Assessment of Creative Thinking in PISA 2021 (Third Draft)

This is a long entry—much longer than I normally post—that in effect summarizes the PISA Creativity Framework, the theoretical foundation for an international test designed to assess creativity. This post begins with an overview of the assessment, then moves into a description and exposition of creativity as a human activity. Very interesting! Long, but definitely worth a read if you find the nature of creativity intriguing…

The Creative Thinking Framework is a product in active development by PISA, the Programme for International Student Assessment. It is a project of the Organisation for Economic Co-operation and Development (OECD), an international nonprofit organization that aims to establish evidence-based international standards for a range of social, economic, and environmental challenges, including education. PISA’s work on Creative Thinking is one of numerous projects that measure 15-year-olds’ ability to use their academic skills to meet real-life challenges. According to its website, “The PISA 2022 Creative Thinking assessment measures students’ capacity to engage productively in the generation, evaluation, and improvement of ideas that can result in original and effective solutions, advances in knowledge, and impactful expressions of imagination” across a range of contexts or domains.

The third-draft framework presents the foundation for the design of the creativity assessment, a carefully crafted two-hour-plus test that uses a variety of means to measure creativity in four domains: written expression, visual expression, social problem-solving, and scientific problem-solving. In each domain, students complete open tasks that have no single correct response. They are either asked to provide multiple, distinct responses, or to generate a response that is not conventional.  These responses can take the form of a solution to a problem, of a creative text, or of a visual artifact.  The test is designed to provide policymakers a valid, reliable, and actionable measurement tool to help make evidence-based decisions in policy and pedagogy in compulsory/public schools.

When complete, the assessment will be provided to 15-year-olds in numerous countries as part of a larger 11-country study of ways to teach and assess creative and critical thinking. (It is unclear whether the assessment was delivered or not; it was supposed to happen in 2021, but was delayed to 2022 due to COVID. The website states, “The worldwide launch of the PISA 2022 Creative Thinking results will be held in 2024,” so presumably it did occur but I was unable to easily find any evidence as such.) PISA is designed not to single out creatively gifted individuals, but rather to describe the extent to which students are able to think creatively when expressing ideas, and then to relate this capacity to various aspects of school environment/systems. The assessment itself is comprehensive and well thought-out and is worthy of much consideration, but what is equally worthy of close consideration is the group’s theoretical foundation and its listing of evidence-based underpinnings of human creativity—and specifically, adolescent creativity.

The PISA assessment measures creativity in three primary domains:

  • Creative Expression: This consists of both verbal and non-verbal forms of engagement, where individuals communicate their internal world to others. Verbal is written and oral; non-verbal is drawing, painting, modeling, music, movement, dance, drama.
  • Knowledge creation: This is the advancement of knowledge where emphasis is placed on progress, not achievement—but improving upon existing ideas, better explanations or theories. It’s about reconstructing knowledge, reinterpreting the findings of others, or making new sense of existing theories.
  • Creative problem-solving: Characterized by novelty, unconventionality, and persistence in solving complex problems.

The PISA framework is 55 pages long, including 10 pages of citations to support its every assertion. This literature review will summarize and condense the framework’s key evidence points and present in simpler form the fundamental evidence-based tenets of human creativity and creativity in education, but will not detail aspects of the assessment or its design. Citations are not consistently indicated below, but readers seeking more information are encouraged to review citations in the third-draft framework document, which is available on the PISA webpage.

Why assess creative thinking?

1) Societies depend on innovation to address emerging and increasingly urgent challenges in a rapidly changing world. 2) Improvements to creative thinking—engagement in the thinking processes connected with creative work—are associated with improvement in other thinking processes, metacognitive practices, identity development, problem solving, and career success and social engagement. 3) Developing an international assessment can help support changes in education policy and pedagogies.

What is education’s role in creative thinking?

Education equips students with the skills they need to succeed in society, and creative thinking is a broadly accepted key skill for success in the 21st century economy, in particular because it helps individuals adapt to a world that changes at lightning speed, thanks in large part to exceedingly rapid advances in technology as well as global environmental challenges that may require unforeseen skills to address. Schools play an important role in helping young people identify and develop their unique talents. Creative thinking can help students interpret experiences and events in personally meaningful ways, and it has shown to be of particular assistance to students who show little interest in school. Creative thinking is not a fixed “personality” asset, but rather something that can be developed over time—and teachers need tools to help teach it to their students through a variety of content areas. 

What is creative thinking?

PISA’s definition is explicitly designed to apply to 15-year-old students around the world. They define it as, “the competence to engage productively in the generation, evaluation, and improvement of ideas, that can result in original and effective solutions, advances in knowledge, and impactful expressions of imagination.” Most interesting is that they define it as a competency, not an immutable personality trait. A second definition they cite from Plucker, Beghetto, and Dow (2004), is worth noting, as it highlights the social nature of creativity: “the interaction among aptitude, process, and environment by which an individual or group produces a perceptible product that is both novel and useful as defined within a social context.”

Following from the creative thinking is the creative product. The product piece requires a set of attributes and skills, including “intelligence,” domain knowledge, and or artistic talent—which I would refer to as technical abilities to bring to life what is a reflection of an inner vision—what PISA calls, “the expression of one’s inner world.”

There is a differentiation between Big C creativity and little c creativity. Big C creativity is associated with technology breakthroughs or artistic masterpieces—evidencing that the creative thinking must be paired with significant talent (talent, being a somewhat problematic term… I might choose “skill” because it is something that can and must be developed), deep expertise, and high levels of engagement—and the recognition from its ambient society that the product has value. Little c creativity refers to more everyday tasks: cooking a great meal without a recipe, scrapbooking, problem solving in scheduling at work. The literature suggests that little c creativity can be developed with education and training, and thus focuses its assessment on that aspect of creativity. It does not focus on Big C creativity, because there is a presumption that that level of creativity relies on some sort of innate, “God-given” talent or high intelligence. The development of little-c creativity matters in education because it can lead to not only the expression of one’s inner world (the arts), but also to other areas where idea generation matters, such as investigating answers to issues, problems, or society-wide concerns.

Domain specificity or generality

Researchers have long debated questions about domains: Is creativity domain-specific or are creative people creative in all domains? Is creative thinking different in science than it is in the arts? Early researchers (1950s) assumed it was not different, but more recent research (2011-20xx) suggest that creativity is indeed domain-specific.

Domains of creative engagement

How many domains are there? Researchers have repeatedly tried to categorize creative domains, notably among them J. Kaufman. His most recent work distinguishes five domains: everyday, scholarly, performance, scientific, and artistic. Others have reported similar groups, and most distinguish between scientific and artistic, but instead suggest problem solving, verbal, artistic, maths. Verbal and artistic were found by Conti to have no correlation whatsoever. Meta-analysis of empirical studies support that math/science and artistic/other forms of creativity are consistently distinct.

Confluence approaches of creativity

Confluence approaches (Amabile, Lucas) describe creative thinking as multi-dimensional, involving four components necessary for an individual to produce creative work: domain-relevant skills, creativity-relevant processes, task motivation, and a conducive environment. Sternberg and Lubart’s “investment theory of creativity” suggest six dimensions:  intellectual skills, domain knowledge, thinking style, motivation, specific personality attributes, supportive environment. Sternberg later showed that the varied degree of each of these individual elements leads to a variety of outcomes.

Understanding and assessing creative thinking in the classroom

Confluence approaches acknowledge the importance of a combination of internal and environmental resources for successful creative engagement. As such, attending to the everyday school environment is important for ensuring creative development in the schools. Schools can nurture creativity by developing a variety of skills: cognitive skills, domain skills, openness to new ideas, willingness to collaborate, persistence, self-efficacy beliefs, and task motivation.

Social environments conducive to enabling creative development are: classroom culture, the educational approach of schools or school systems, the broader cultural environment. All of these influence the way students value creative ingenuity. Schools provide a helpful “habitat” for creativity conducive to measurement and assessment, and the school environment, as it sits within the system and the culture, enables the way in which individual creative motivations are developed and refined.

Individual Enablers of Creative Thinking  

Cognitive Skills

Guilford’s 1956 concepts of convergent and divergent thinking have influenced creativity research. We might describe convergent thinking as “algorithmic,” whereas divergent thinking refers to the ability to produce novel ideas from unexpected combinations of available information. Most measures of creative thinking to date have focused on assessing levels of divergent thinking processes, but research has also shown that convergent thinking can also contribute to creativity. In other words, creative individuals who can think outside the box also show evidence of knowledge and skills from an “inside the box” perspective.

Domain Readiness

This means that individuals require some degree of skill and prior knowledge within a domain to produce creative work. However, this is neither universal nor linear. In some individuals, knowing the rules within a domain can block creative attempts to break them.

Openness to experience and intellect

The likelihood that an individual who “knows the rules” will be willing to break them depends on personality traits that many studies have shown characterize creative people—openness to ideas and openness to experience. Openness was shown to be the only “Big Five” personality traits that consistently applied to creative people, and this was validated across several cultures and countries. Divergent thinking also has been shown to positively correlate with openness. Other researchers have found that openness-related traits such adventurousness, willingness to be “uncomfortable” and curiosity for noveltly also apply to creative individuals.

Goal orientation and creative self-beliefs

Individuals who think they are creative tend to be more creative, coupled with persistence and perseverance. Efforts to stimulate creative thinking in the classroom should aim to help students believe in their own abilities and teach the value of consistent, sustained effort.

Collaborative engagement

Contemporary thinking is looking beyond creativity as an individual construct more toward a collective endeavor. Research is looking at the benefit of collaboration and teams in developing creative ideas. “Through collaborative engagement, teams can provide new answers to complex problems that are beyond the capabilities of any one person.” (Warhuus et al, 2017) Being able to engage in dialog and idea sharing creates fertile ground for the growth of new ideas. Schools can stimulate creativity in individuals by allowing for classroom collaborations that will engender new knowledge.

Task motivation

Much research supports the role of task motivation in creative productivity. Motivated individuals find their work meaningful, enjoy the work, desire to be challenged, and are relatively immune to outside pressures. This is related to the concept of a “flow” state in which an individual is lost in the single-minded desire toward its creative product.

Social enablers of creative thinking

Cultural norms and expectations

Cultural norms and expectations can shape the skills and knowledge that individuals prioritize. They can also encourage creative thinking in some situations, but discourage it in others. Several studies have concluded that a culture’s sense of individualism vs. collectivism has a significant impact on creative outputs of individuals.

Educational approaches

Classroom environments can stimulate and encourage creativity by increasing the rewards and decreasing the social costs associated with creative thinking. They must release emphasis on standardization and assessment.

Classroom climate

Informal feedback, goal setting, positive challenges, teamwork, autonomy, and recognition and encouragement all enable creativity. Harsh criticism, focus on status quo, low-risk attitudes, and high time pressure stymie creativity. School poliices that can inhibit creativity include: 1) only one right way/answer, 2) submission and authoritativeness, 3) adherence to lesson plans at all costs, 4) promoting the belief that originality is rare, 5) promoting beliefs in compartmentalization of knowledge, 6) discouraging curiosity and inquisitiveness, and 7) never letting learning be fun. Teachers are more likely to promote creativity in their classroom if the larger school/system values innovation. Important traits for teachers to stimulate: risk taking, idea diversity, collaboration with peers. Teachers must believe that creative thinking is a competency that can be developed in the classroom.

Teachers can encourage creativity by allowing students to set their own goals, monitoring progress, identifying promising ideas, and taking collective responsibility for productive, creative team work. They also need to teach students to distinguish when creativity is appropriate.

Creative engagement

Creative products provide indicators of creative thinking; these products can be used to determine the level of creativity in their thinking. Based on a variety of research, products are deemed creative when they are novel and useful as defined within their social context. In school, creative engagement takes the form of expression thru writing, drawing, music, or the arts; new knowledge and understanding; creative solutions to problems.

Key Points in Assessing Creativity: Implications for the PISA 2021 creative thinking assessment design

The following points are drawn from the frameworks’s implications section and are listed here because they are key in determining any student’s creativity:

  • Key points for measuring/assessing creative expression: originality, aesthetics, imagination, affective intention (emotional expressiveness) and response
  • Creative writing (whether fiction or nonfiction) is an expression that requires both cognitive and communication skills, logical consistency, details, and continuity.
  • Visual expression requires that students communicate ideas using visual media and helps students decode both overt and subtle images. Visual communication is increasingly important in a digitized world.
  • Social problem solving. This develops skills in understanding and address the needs of others to find solutions to key problems.
  • Creative scientific problem-solving focuses on the generation of new ideas, rather than on applying taught knowledge.

The Competency Model of Creative Thinking

This graphic indicates the competencies of creative thinking in four major domains; this illustrates the assessment areas that PISA covers.

  • Generate diverse ideas: How many solutions or ideas can an individual generate? Also referred to as “ideational fluency.” Also, how diverse are those ideas?  AKA “ideational flexibility.”
  • Generate creative ideas: As compared to what—new to you, or new to the larger population? The assessment compares uniqueness in the context of the population taking the test. To what extent does the student draw upon multiple domains in solving a complex problem or generating new ideas?
  • Evaluate and improve ideas: Creativity is not always about deviating from the usual, but rather novel ideas that are effective for intended purpose. Can the student identify limitations and solutions to improve them?

Final key points regarding assessing creativity.

The PISA creativity assessment itself is coupled with a survey designed to determine each student’s engagement with four categories of creative “enablers.” These are measured by self-report. The contextual factors for creativity as covered in this survey are:

  • Curiosity and exploration: Individuals’ creativity is affected by individual curiosity, their openness to new experiences, and their disposition for exploration.
  • Creative self-efficacy: Do students believe in their own creativity? How confident are students about their ability to think creatively in different domains?
  • Beliefs about creativity: Do individuals believe that they can be trained, or that creativity is an immutable personality trait? Do they think creativity only exists in the arts? Do they believe it’s a good thing in all contexts?
  • Creative activities in the classroom and school: Do students participate in school activities and out-of-school that require and develop their creative capacity? Are they regularly asked to perform or generate new ideas?
  • Social environment: Is free expression encouraged? Do authority figures (teachers) take students’ new ideas seriously? Does the larger environment value novelty and expression?

Autonomy support, personality, and mindset in predicting academic performance among early adolescents: The mediating role of self-determined motivation.

CITATION: Mammadov, S., & Tozoglu, D. (2023). Autonomy support, personality, and mindset in predicting academic performance among early adolescents: The mediating role of self-determined motivation. Psychology in the Schools, 60, 3754–3769. https://doi.org/10.1002/pits.22966

This paper supports a tenet that is highly unsurprising: Young adolescents—that is, middle school students—who are very conscientious and who have high levels of autonomy support from teachers and parents show greater intrinsic motivation and as a result tend to do better academically. This research set out to determine not whether, but rather the degree to which these conditions are correlated with academic outcomes in middle schoolers.

Using Deci and Ryan’s self-determination theory as a theoretical framework, the authors used a series of questions focused on the theory’s three basic needs: autonomy (desire to feel ownership over one’s actions, competence (desire to be good at something), and relatedness (desire to form and maintain strong bonds with others). Autonomy in particular is a predictor of academic success, and this paper cites numerous sources that indicate that students thrive academically and socially when their behaviors are autonomous rather than controlled.

The second part of the authors’ theoretical framework was the widely recognized Big Five, a personality taxonomy that suggests that five primary traits predict personality: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The first two have been strongly correlated with learning in innumerable studies, and as such, were appropriate choices for personality within this study.

The authors presented an 86-question Qualtrics survey to 234 middle school students from a public school district in the southeastern US; students were mostly white (58%), which reflects the distribution in that school district. The Likert-style survey asked a series of questions clustered around determining conscientiousness, openness, growth mindset, teacher autonomy support, parent autonomy support, and motivation. Survey results were compared against end-of-year cumulative grade point averages. Conscientiousness had the strongest correlation with academic performance, growth mindset a mild correlation, and the personality trait of “openness,” the weakest. One surprising result was the connection of controlled motivation with grades—that is, middle school students’ academic outcomes appeared connected to external motivators such as grades, recognition, perceived approach, or to avoid punishment or feelings of guilt. The most important and applicable finding was the “critical” importance [sic.] of autonomy support. Students who receive high degrees of autonomy support from teachers and parents tend to do better academically.

One limitation is the connection of conscientiousness, openness, and growth mindset (optimistic self-efficacy) as personality traits, rather than fluctuating emotional conditions. Ascribing these to “personality” suggests that they are immutable, and much research supports evidence to the contrary. Second, though the authors note correlations between the measured traits and conditions, they self-state that this study cannot confidently assert causality. A third limitation is that the personality indicators were all determined by self-report and thus potentially influenced by social desirability, but note that self-report survey is standard practice in qualitative assessment of personality traits and motivations for behavior.

Profiles of personal and ecological assets: Adolescents’ motivation and engagement in self-driven learning

Zhu, G. and Burrow, A. (2021). Profiles of personal and ecological assets: Adolescents’ motivation and engagement in self-driven learning. Current Psychology 42:14025-14037. https://doi.org/10.1007/s12144-021-02412-0

Opportunities for self-driven, informal learning for teens are on the rise, but little is known about what motivates students to seek these activities, nor about what keeps them engaged when they find them. In this study, researchers evaluated student motivation and engagement via a large and diverse sample of teens (N=580, Mage=16.5) via GripTape, a self-driven learning program where ethnically and socioeconomically diverse students are provided modest grants to choose their own topics and explore their own direction. According to its website, “GripTape is a 501(c)(3) nonprofit that offers young people ages 14–19 total control to design a 10-week learning experience, up to $500 to pursue this learning, and a Champion to support them.”

Via several Likert-style surveys based on Deci & Ryan’s Self-Determination Theory, the researchers sought to identify each student’s personal (persistence, confidence, resilience) and ecological (adult support, safe environment) assets and classified them as either High Assets or Low Assets. Students were also surveyed on their self-reported rating of positive learning experiences, perception of support, assessment of meaningful knowledge/skills, and commitment to learning.

They found that high-asset students had significantly greater levels of intrinsic, extrinsic, and competence demonstration motivations than low-asset students, but no difference in engagement level in their work, the latter suggesting that all participants felt equally satisfied with and engaged in the work they were doing. They also found that external motivators (achievement, grades, competence demonstration) negatively correlated with youth’s motivation for and engagement in self-driven learning, meaning that students who were motivated by external factors were less engaged and less committed to their learning. The researchers conclude that their study provides empirical evidence that teens maintain high intrinsic motivation when provided with opportunities to direct their own learning in topics they are interested in. The paper cites ample research that indicates that higher engagement and motivation predict positive learning outcomes, from which the reader can infer that more confident students with more supportive resources achieve better learning outcomes in self-driven learning.

This is an excellent, well-structured and clear study that includes many relevant and helpful citations throughout. It highlights the importance of self-direction and relevance to teen students’ engagement in learning, and also shows much evidence that supports the critical importance of adult support and safe environment to teen engagement and learning. It demonstrates the contributions of relevancy, purpose, and ownership to learners’ positive learning experiences and engagement (defined herein as “commitment”) to learning topics. It also shows that extrinsic motivation negatively correlates to engagement.  The only limitation of this study that could be subjected to further research is that it was entirely survey-based, and the data was not compared to any objective measures of student performance.

Link to full article.

Fledgling Thoughts on AI from Four Assigned Readings

Photo Credit: Tierney – stock.adobe.com

The following shares some thoughts on AI in education, based on four scholarly papers on the subject. The content below includes two big ideas that stood out per article, followed by one(ish) sentence on how it changed what I think of AI or how it might apply to my work. Truth be told, I have done very little reading in AI, so nearly everything I read changed my thinking. The reflections below represent the concepts that stood out most to me.

Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38-51.

SUMMARY: The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AI-Ed debate.

  • The following is a paraphrase quote: Despite the consensus that AI should be ethical, there is also disagreement on what comprises ethical AI, and the ethical criteria, technical standards and protocols necessary for its implementation. The values attached to many of these systems are deeply intertwined with the values of those who create it, and these do not necessarily translate to all contexts and traditions.  “Intercultural information ethics scholar Pak-Hang Wong warn, there is a danger of dominance of ‘Western’ ethics in AI design … to the exclusion of other value systems…” With this in mind, when AI systems designed external to the context within which they are to be introduced, whose knowledge becomes privileged when said systems are fully integrated into education through policy and practice? This is especially important when thinking about education as a space where ideology is passed down (IEEE, 2018).
    REFLECTION: Whose knowledge is privileged? Terrifying?
  • First of all is the design and development of resources to use education as a tool to contribute to poverty reduction. This trend hinges on the importance of education for national economic growth and development. …Education is not only seen as a good that nurtures human flourishing; it is also a sector from which significant profits can be made, especially in light of innovations in AI educational technologies. AI in education is a path to a global market share valued at US$1.1 billion in 2019, and it is expected to reach US$6 billion in 2024 and US$25.7 billion by 2030 (Holmes et al, 2021).
    REFLECTION: AI in education is driven by market interests, and the development of STEM skills has been tightly connected by many major international organizations to economic dominance. Why does this surprise me?

Sofia, M., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science: The International Journal of an Emerging Transdiscipline, 26, 39-68.

SUMMARY: This paper aims to investigate the recent developments in research and practice on the transformation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges that arise. Excellent paper!

  • Before 2030, it is estimated that 375 million people (14% of the global workforce) may need to change jobs due to AI-related technological advancements. It is estimated that the share of key skills will change by 40% in the next five years, and 50% of all workers will need retraining and further education (World Economic Forum (WEF), 2020). Key skills that are expected to increase in importance by 2025 include technical skills critical for the effective use of AI systems and soft skills (also called transversal skills) such as critical thinking and analysis, problemsolving, and self-management (WEF, 2020).
  • Artificial General Intelligence: AGI could have a significant impact on human skills in organisations, either by automating tasks currently requiring human intelligence and problem-solving abilities and the need for workers to acquire new skills or retrain for different roles, or by augmenting human intelligence and problem-solving abilities, leading to increased productivity and efficiency, as well as the opportunity for workers to focus on more complex tasks requiring higher-level thinking.
  • Even if the scope of these tasks and the intelligence required can vary from job to job, as AI has pushed back mechanical labour, humans will need to focus on tasks that AI is unlikely to take on, namely those that require “thinking” and “feeling” skills (Huang & Rust, 2018; Huang et al., 2019).
    REFLECTION: Especially in medical diagnosis,I can see how AI could help a service provider synthesize the massive amount of information available that is impossible to keep in one’s mind at all times, especially staying up to date on new research. AI can serve as a cognitive assistant while the human person can focus on the feeling skills at work—compassion, care, warmth, connection.
  • AI can help organizations automate some processes, but employees still need to use their creativity to come up with new ideas, think outside the box, and solve problems that AI systems cannot. AI systems can save time by automating or speeding up the time-consuming or repetitive low-level tasks, leaving employees to use their brainpower to focus on tasks requiring creativity, innovation, empathy, or other qualities that are unique to humans.
    REFLECTION: I hadn’t thought through some of the potential benefits of this, though I do think that sometimes it’s good to have those mindless tasks to prime the mind for bigger, more creative endeavors. The human brain is not typically able to be creative 100% of the time. The balance of mindless tasks fills the gap and helps make the “non creative” time continue to be productive.

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI?. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 9795-9799).

SUMMARY: This article presents the findings of a joint task for on AI in education as presented at The Thirty-Third AAAI Conference on Artificial Intelligence in 2018. According to and implied in the report: Because AI is the newest disruptive technology that is shaping the future (and present) of AI (what many call “the fourth industrial revolution”), it is prudent for countries concerned with maintaining economic dominance to follow China’s lead and ensure that its citizens are educated about AI, both as users and potentially as developers. This document outlines 2018 US national guidelines for teaching AI to K-12 students made by a joint working group of the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA).

  • Understanding people is one of the hardest problems faced by intelligent agents. This includes tasks such as conversing in natural language, recognizing emotional states, and inferring intentions from observed behavior. Students should understand that while computers can understand natural language to a limited extent, at present they lack the general reasoning and conversational capabilities of even a child. In grades 3-5 students should be able to distinguish a chatbot from a human, and analyze natural language examples to determine which ones would be difficult for a computer to understand, and why.
    REFLECTION: Adults should too!
  • “In grades 3-5 students should be able to distinguish a chatbot from a human, and analyze natural language examples to determine which ones would be difficult for a computer to understand, and why. In grades 6-8 students should be able to use parser demos to demonstrate syntactic parsing of sentences, and construct sentences that purely syntactic parsers will mishandle due to problems such as erroneous prepositional phrase attachment (e.g., ‘I pour syrup for pancakes from a bottle’).”
    INTERESTING: Human knowledge of grammar will still matter!

Deruty, E., Grachten, M., Lattner, S., Nistal, J., and Aouameur, C. (2022). On the Development and Practice of AI Technology for Contemporary Popular Music Production. Transactions of the International Society for Music Information Retrieval, 5(1), 35–49. DOI: https://doi. org/10.5334/tismir.100

SUMMARY: A team from Sony Computer Science Laboratories (CSL) in Paris provided recently developed Sony AI tools and prototypes to six artists. The artists applied the tools to music creation, and then shared feedback on what they liked and did not like, alongside observations on how it affected their creative process. The paper presented recommendations for the use of AI in contemporary popular music based on artist feedback.

  • AI-generated ideas helped “break creative habits” and allowed the artist to “reflect on one’s own creative practice and aesthetic values.” This resonates with thinking on the nature of creativity, which has moved beyond seeing creativity as a function of individuals but rather stemming from interactions between individuals in a social environment according to conventions and attributes of a specific domain. This idea has important implications for teaching creative endeavors in an online setting, and points to the critical importance of finding ways to increase student-to-student interaction in creative projects through group work or other forms of cooperation and idea sharing.
    REFLECTION: What role can AI play in increasing student interaction… or… gasp… serving as a proxy for student interaction?
  • Many issues may surround the use of authorship and creative ownership when using AI in-studio music creation. AI further complicates an issue that is already in play. “Composition” and “ownership” today is more complex than the traditional more linear view, which involves a single composer or artist(s) realizing their vision with the help of producers and engineers. Now, music is created in-studio and the final production emerges as a team effort of the musicians as well as the multiple individuals engaged in the production and editing process.
    REFLECTION: What happens when we add AI to the composition mix? Artists in this article expressed interest in having AI as a tool that they would incorporate into their creative process—but not replace it. Who benefits from the creation of an AI system that operates autonomously—creating music independently of any inputs from human artists?

On the Development and Practice of AI Technology for Contemporary Popular Music Production

[Photo: Berklee Online.]

ANNOTATION: Deruty, E., Grachten, M., Lattner, S., Nistal, J., and Aouameur, C. (2022). On the Development and Practice of AI Technology for Contemporary Popular Music Production. Transactions of the International Society for Music Information Retrieval, 5(1), 35–49. DOI: https://doi. org/10.5334/tismir.100

The music industry has been abuzz with discussion on AI’s entry into contemporary popular music creation. It is used in a number of ways, including: to create new lyrics, to synthesize individual sounds, to break recorded tracks into their individual parts (referred to as “stems”), and to create complete musical tracks. The question is, which parts of music creation will AI be most useful and relevant for, and how can it best be included in a musician’s creative workflow?

To answer this question, a team from Sony Computer Science Laboratories (CSL) in Paris provided recently developed Sony AI tools and prototypes to six artists. These tools included either standalone applications, VST plug-ins for digital audio workstations (DAW), or servers accessible through a web-interface. These tools offer AI contributions to a variety of aspects of the music production process, from sound design to mixing, equalization, and the generation of melodic and rhythmic material. The tools are primarily pull interactions, where the user explicitly queries the tool for an output using priming—for example, serving as the start of a musical part to be continued by the tool, as the starting template from which variations can be explored, or as a part in a multi-part setting for which the tool generates accompanying parts.

Though there are several contexts in which AI tools might be used to create music, this paper focuses on in-studio music creation, which today is more complex than the traditional more linear view, which involves a single composer or artist(s) realizing their vision with the help of producers and engineers. Now, music is created in studio and the final production emerges as a team effort of the musicians as well as the multiple individuals engaged in the production and editing process.

The six artists readily participated in this study not as “subjects” but as an opportunity to use the tools in music creation and then provide feedback. The artists were offered the tools and then interviewed about what they liked and did not like, and provided observations on how it affected their creative process. Artists answers were categories and subjected to a thematic analysis, all guided by the overarching question: how do artists use the AI tools?

One standout comment was that the AI-generated ideas helped “break creative habits” and allowed the artist to “reflect on one’s own creative practice and aesthetic values.” This resonates with thinking on the nature of creativity, which has moved beyond seeing creativity as a function of individuals but rather stemming from interactions between individuals in a social environment according to conventions and attributes of a specific domain.

The researchers identified the following four primary lessons learned from this study:

  • AI in music creation should work alongside artists. The artists in this case study were happy to use this as a tool to create ideas, but not a replacement for their own creativity.
  • A good tool affords the possibility of unexpected results. For example, the “glitchy” and “imperfect” parts of music created by AI were interesting to artists because they created unexpected effects that contributed to interesting ideas in the final musical product.
  • AI can be a part, but not all of a production. Artists still want to be part of the process.
  • Adapt to the music at hand—meaning, create products that are familiar to the workflow that artists are already used to in contemporary music making.

To determine the validity of AI in music production—in other words, to develop a product that musicians will actually like and use—artists’ feedback suggested the following: Any effective AI tool needs to integrate smoothly into existing workflows, make complicated tasks easier not harder, enhance creativity and provide ideas but not replace them, have clearly identifiable results, and be publishable. If these six artists were representative of the larger population, it is clear that AI may have a role in helping to make complicated tasks more simple, and provide creative fodder and help artists break through creative blocks, but if any AI product is to be adopted widely, its developers need to ensure that the artist feels that they are part of the creative equation.