Exploring Generative AI in Teacher Preparation Call for proposals

 Title/Theme: Exploring Generative AI in Teacher Preparation

The Challenge 

Generative AI is rapidly becoming commonplace and coupled with the availability of personal devices and one-to-one technology adoption, we need to ensure that the current and future generations of teachers understand its implications, know how to adjust their pedagogy and how to use it to assist in lesson planning, assessment, and individualizing instruction. In this call, we are specifically inviting submissions from practitioners using evidence-based strategies in both pre-service and in-service teacher education. 

Submissions might focus on (but are not limited to): 

• Personalized Learning 
• Intelligent Tutoring Systems 
• Automated Grading 
• Data Analysis and Insights 
• AI-driven Simulation and Virtual Reality in Teacher Education 
• Feedback on teacher performance 
• Lesson and assessment planning 
• Inclusion and accessibility 
• Chatbots in Learning and self-regulation 
• Bots for socio-emotional learning 
• Adaptive learning 
• AI literacy for teacher educators 
• What do teachers need to know in a world of Generative AI 
• Teacher preparation in an age of Generative AI
• Whose data? Who is learning? The complex realities of learning in an age of Generative AI 
• Ethical and Equity Implications of Generative AI in Teacher Education 
• The Economics of Generative AI and Teacher Education 
• Cultural Sensitivity and the Deployment of AI in Diverse Educational Settings 
• Assessing the Impact of Generative AI on Accessibility and Inclusion in Teacher Education 
• Generative AI, Social Justice, and Educator Preparation. 

The Approach: 

In addition to an open call for proposals, we also intend to invite scholars to submit articles from those who have participated in events held by the AACTE Committee on Innovation and Technology (I & T Committee). Since the spring of 2023, the I & T Committee has held a series of webinars and online Lunch and Learn sessions focused on generative AI in teacher education. Researchers and practitioners familiar with AI tools shared policies, procedures, and practices with the AACTE community, leading to rich forward-thinking conversations about this timely topic. We will continue to hold these events leading up to a featured session at the AACTE 2025 Annual Meeting in Long Beach, CA, where some of these scholars and I & T Committee members will be presenters. 

• Editors:
  Valerie Hill-Jackson, Ph.D., Texas A&M University
  Cheryl Craig, Ph.D., Texas A&M University
• Guest Co-Editors:
  Guy Trainin, Ph.D., University of Nebraska- Lincoln
  Laurie Bobley, Ed.D., Touro University
  Punya Mishra, Ph.D., Arizona State University
  Jon Margerum-Leys, Ph.D., Oakland University
  Peña L. Bedesem, Ph.D., Kent State University

Manuscript Guidelines 

Authors are encouraged to submit manuscripts that meet the following criteria: 

• All manuscripts must be fully blinded to ensure a reliable review process. 
• All manuscripts must meet publishing guidelines established by the American Psychological Association (APA) Publication Manual (7th edition, 2019). 
• A manuscript, inclusive of references, tables, and figures, should not exceed 10,000 words. 
• No more than one manuscript submission per author. 
• Read more JTE guidelines. 
• To submit your manuscript, please visit the JTE website. 

Timeline for Submission 

• June 15, 2024: A 150-word bio for each author, a 300-word structured abstract, and 5 keywords due to guest editors. Email these items to jmleys@oakland.edu and the subject line should read: ‘JTE Anniversary 76(3) – Abstract’. 
  
• September 1, 2024: Manuscript submission deadline for ‘Level 1’ external review; see the above guidelines. Manuscripts need to be in ‘near publication’ quality to move forward to the Level 2 review. 
  
• November 15, 2024: Level 1 – External peer review completed. 
  
• December 10 through January 10, 2025: ‘Level 2’ review by guest editors; feedback is provided to prospective authors on a rolling basis. 
  
• Noon (CST) Saturday, February 1, 2025. All final manuscripts must be received in the Sage online system for consideration of publication in JTE’s 75th anniversary issue on Generative AI, 76(3). The publication date is targeted for May 2025. 

The Yin of AI in Education

 

Last Friday I had the chance to be part of a panel on AI at the Carson Center for Emerging Media Arts as part of a larger symposium (more here). It was a great event and I leaned a lot from the main speakers. After the morning speakers set a somewhat somber tone for the potential outcomes we were asked to try and present some of the positive outcomes that might emerge from AI (not just generative) in our respective fields.

I brought up three possible contributions to education:

1. Making teachers' lives easier. Easing the pressure on teachers by providing strategies that help reduce workload in non-instructional tasks such as assessment scoring, planning, letter and parent newsletter writing, etc. This does not replace the need to actually reduce the workload by shifting demands but augments it in ways that will free teachers to focus on what they do best—teaching students.

2. Creating differentiated plans. While curriculum authors and teacher education provide many ideas about how to differentiate instruction, the workload to differentiate instruction for every relevant lesson can be quite significant depending on class size and variability. An AI that can learn from assessment and teacher planning can become an excellent companion, allowing for robustly differentiated instruction with a record that can potentially move with students to subsequent grades or new educational environments (for example, mobility between schools). 

3. Tutoring students and supporting less qualified teachers. The Global South has been experiencing teacher shortages in rural areas, and these shortages are expanding worldwide. Tailored AI can support less qualified teachers and tutor students. While this situation is less than ideal, AI can fill in the gaps until we can create better systems to support teaching.

For these to be successful, school systems must be able to create sequestered, safe instances of AI that can be tailored and protective of student, family, and teacher data. Without such instances, schools should not use AI systems in any way that has access to student data. The goal for researchers should, therefore, be creating these instances through specialized API and examining its impact on teachers and students.

Leaving Las Vegas thinking about Computer Science Education

The SITE conference was in Las Vegas this year. It was great to connect with old friends and find new ones. While on the plane home, I want to reflect on what I learned before the hubbub of teaching and my next conference arrives. 

What did I see? I chose to focus on the work being done in Computer Science. The panel put together by Chrystalla Mouza was especially excellent. The panel had a great discussion of strategies and challenges in recruiting and retaining teachers for CS. The metaphor I settled on was the blanket that is too small. If a specific district is able to "recruit" from another school system, the problem does not get solved. It just changes location. The same can be said if we transition Math or science teachers. The three strategies that emerged were:

1. Recruit internally from areas that have a enough teachers (e.g. art, Media, English or Social Studies). For this to be successful the professional learning has to be different and address ways of thinking and projects of interest that would fit different thinkers within their domain expertise.

2. Make it part of a general campaign to go into teaching. 

3. Focus on second career/ career changers. Here there is a need for short programs and funding during the process of changing career.

The second strand was TPACK which after over 15 years of research is still one of the most often used frameworks. Punya Mishra led a series of presentations sharing the work done on the third handbook of TPACK research soon to come out. The work was varied and interesting and the variety of approaches, measurements and direction was extensive. 

Finally, and unsurprisingly there was discussion and grappling with AI everywhere I went. AI is changing everything including education in ways that we do not fully understand, but researchers from around the world are trying to apply what we learned from previous critical moments (advent of personal computing, internet, social media). 

What am I using AI for now as a Teacher Educator and Professional

 Since Generative AI came out, I have been using it extensively. As an exercise, I am logging all the direct Generative AI I use, knowing that there is much AI in the background of which I am less aware.

Generic letters: Looking at my log, I have used generative AI to create four official letters that required carefully worded messages that were sensitive yet firm. In each case, I used Chat GPT to create an initial wording, then edited the text to bring back my writing style and some of my personality when appropriate, and finally, I ran it through Grammarly to make sure that I had no embarrassing grammar and spelling errors. The use of generative AI for composing official letters creates great efficiencies for me and reduces the response times. Interestingly, one person asked me for a letter of support that they generated with the help of GenAI as well as a starting point.

In teaching: I have used ChatGPT to create a description of the social networks between students in a classroom for an activity on creating groups in an elementary classroom. Once again, I needed to refine the prompt a few times and finally edit the document, but the result was quite good, and I created an assignment that I will keep using in the future.

I tried to see what Gen AI would produce for an in-class presentation about reading instruction. The result was VERY generic, and I ended up discarding the suggested slides, retaining the I Dall-E to create unique artwork for the slides I designed for teaching writing. While Generative AI use was limited in creating content, I continue enjoying the use of the Designer feature in PowerPoint as a way to quickly spiff up my slide decks. Since we came back from Spring break, I created a set of questions for a welcome-back exercise that went very well.

Finally, I engaged my students in using GenAI to create groupings in their classroom (mock data) to see what the benefits and challenges are. The discussion that ensued included comments ranging from amazingly fast and accurate to a student questioning whether it is worth the time after a lot of editing.

Review of academic paper: Once I read the paper I was reviewing and had the main points that I wanted to stress to the authors so they could improve their research paper, I used Cen AI to expand and explain my bulleted points. The amount of editing this exercise created for me was a very limited return on investment, and I doubt I will use it in this way again.

Podcasting: I used GenAI to create episode summaries of the Not That Kind of Doctor podcast using the transcripts as the raw material. One episode summary was well done while ina. second GenAI completely missed the point. Both needed editing but were still a major time-saving application.

Across multiple uses, I usually prompt GenAI there times before I get everything that I want (or give up). More detailed prompts yield much more accurate results and less follow-up. Grammarly let me know that it made over 6000 suggested edits. Gen AI has changed how I work; it has made some things much easier and saves me time every day. However, I am still concerned with accuracy and specificity that can be achieved only through my deep seated professional knowledge.

AI Creativity and the near future of education

AI and Creativity created by ChatGPT-4

This week, I spent two days at the Nebraska Educational Technology Association meeting in Omaha. It was great to meet with friends, colleagues, and new acquaintances. Everyone talked about AI as a catapult to changing, rethinking, worrying, and joy. Evi and I spent some time talking about how we humans are still better/ different than the machines. Much like humans have for centuries argued that humans were not like other animals, our current existential obsessive discussion (and fear) is about what happens when Artificial General Intelligence shows up. For me, the question is what we can do in the short run. The answer may very well be a focus on inquiry, creativity, and self-guided learning.

As highlighted by our work in Art TEAMS, integrating new tools and emphasizing creativity and divergent thinking in education presents a forward-thinking model that could significantly influence schools in the coming years. This approach, which blurs the traditional boundaries between art, sense-making, and metacognition, opens up several intriguing possibilities and challenges for education.

For example, schools might increasingly incorporate digital tools and platforms that foster creativity, such as digital art, coding for creative purposes, and virtual reality for immersive learning experiences. These tools can help students explore complex concepts in a hands-on manner, enhancing their understanding and using AI tools to amplify creativity and self-expression. 

By leveraging AI and other technologies, educators can create personalized learning paths for students. This customization allows students to explore subjects at their own pace and according to their interests, which can boost engagement and motivation. This can happen while we encourage metacognitive skills, thinking about one's own thinking, that can help students understand and regulate their learning processes, strengths, and areas for improvement. This self-awareness is crucial for lifelong learning and adaptability, especially in a rapidly changing world. 

This approach allows for breaking down the silos between subjects, especially art, humanities, and sciences, and encourages students to make connections across disciplines. This holistic method fosters critical thinking, creativity, and the ability to see problems from multiple perspectives. 

To reach this goal, we need to focus on teacher education and mindset shift. Teachers will need training and support to adapt to these new tools and pedagogies. Shifting from traditional teaching methods to a more student-centered, creative, and interdisciplinary approach requires time, resources, and a change in mindset. This can happen during pre-service and in-service teacher education and requires attention to equity and access. Our educational system has a robust tendency to focus on basic skills for those with perceived deficiencies who never get to interact with more complex educational experiences.

 As AI and other technologies become more integrated into education, maintaining a balance between technological efficiency and the human elements of learning—such as empathy, ethics, and emotional intelligence—will be vital. The potential for human flourishing through this educational paradigm is significant. By fostering an environment where creativity, critical thinking, and personal reflection are paramount, students can develop not only the skills needed for future careers but also the capacity for resilience, empathy, and ethical decision-making. This approach not only prepares students to thrive in a world where AI plays a significant role but also ensures they contribute positively to their family, community, and society.

Riding the Tiger- AI and Teaching in Higher Education

 Recently, I was part of a panel on AI in academia. This is just part of the way I re-orient myself in my work. AI is demanding that we pay attention, or we become obsolete. My metaphor is riding the tiger, and with the help of two AI generators (Dall-E and Firefly), I created two images imagining it.

The metaphor is connected to a few crucial concepts. First, we are not being asked whether the tiger (AI) should be introduced into our lives. It has already been released, and it has now become our problem. We cannot ignore it because the tiger can and will harm us. What is left for us is to try to ride it. I am not certain that we will survive, but I am positive that I will enjoy the process. That I will enjoy the process.hat I will enjoy the process.hat I will enjoy the process.

Second, I believe that we should take an AI pause, not from development but instead from teaching. Pause and dedicate time to think through what AI means for our teaching domain. To guide such work, we should have "worked examples" (Gee, 2010) produced by instructors that are being thoughtful and comprehensive in their incorporation of AI.

 

Generative AI and the Science of Reading

Learning to read requires excellent texts that are carefully constructed to combine a few elements, interest, introduction of new vocabulary, and control over the level of difficulty, including the use of specific phonological and orthographic patterns for early readers. This has always been a source of difficulty, book publishers have done a reasonable job, but the costs for schools and classrooms have been prohibitive, especially when considering the need to send students home with texts to practice reading.

TextProject from Elfrieda Hiebert has been producing such balanced texts and making them available for free.

Generative AI presents the opportunity to create such texts readily.

for example I asked chatGPT to create a first grade level text about dogs:

All About Dogs

Dogs are fun friends to have. Some are big and some are small. Some dogs have long hair and others have short hair.

Dogs love to play. They like to run, jump, and fetch balls. Some dogs like to swim in water, while others prefer to stay dry.

They have wet noses and wagging tails. When a dog wags its tail, it is often happy. If a dog shows its teeth, it might be scared or upset.

Dogs can hear very well. They also have a good sense of smell. This is why some dogs are used by police to find things.

It's important to be gentle with dogs. We should give them food, water, and a cozy place to sleep. In return, dogs give us lots of love and joy.

Dogs are not just pets; they are part of the family!

I followed up with a request to make it rhyme.

**Doggie Delight** Dogs are friends, both big and small, With furry coats, short and tall. They love to jump, run and play, Fetching balls and barking away. Some love the splash, some stay neat, With wagging tails and tiny feet. When tails wag, they're feeling glad, But bared teeth mean they're somewhat mad. With ears so sharp, they hear a pin, And noses that sense where they've been. Police dogs with a sense they hone, Find things hidden, all on their own. Treat them right, with food and rest, For as our pals, they are the best. Not just a pet, but family too, Dogs bring joy in all they do!

You will notice that the vocabulary is no longer as simple as in the original text. 

Finally I asked for a rewrite using short vowels:

Pups at Play

Dogs are pals, big and small, With fur that's short, or long and tall.

They jump and run, and play all day, With a ball to toss and fetch their way.

Some dogs swim, some sit and nap, With a wagging tail and a little yap.

With ears to hear and a nose to sniff, They find a scent, then off they zip!

Feed them well and give them a bed, Pet their head, and they're so well-fed.

Dogs are fun, they make us glad, Best little pals we've ever had!

In carefully examining the results, it is clear that ChatGPT can do some of the work for us, BUT we need to make sure that the text is consistently doing what we actually need. Right now, even with prompting, the results need "massaging" depending on what we are teaching. S teachers use but beware!

AI and Academic Publishing

 Like many others, I have been playing with generative AI for the past few months. I am an author of scientific papers and, even more so a frequent reviewer. I have been elated by the potential of generative AI to bridge the gap between English knowledge and conducting high-quality research, especially for international scholars whose first language is not English. This is an opportunity to level the playing field and allow equal access to academic publishing, which is predominantly conducted in English. Many times I have reviewed articles with good ideas but really hard-to-understand language that required many rounds of review and editing before it was publishable.

On the other hand, generative AI is quite as capable of generating data that isn't there (often referred to as hallucinating). For example, after being asked about my publications Chat GPT 3.5 spit out this list: None of the publications are real! This will require our publication engines to allow us to track every in-text reference with quick access so reviewers can check the veracity of such claims that may be "halucinated".

Even more challenging is generative AI's ability to "hallucinate" research studies. In a manner of a few minutes, I was able to have Chat GPT generate two potential studies about reading instruction (synthetic phonics and reading recovery) with ANOVA designs, including result tables. I even got Chat GPT to design and execute a study about the impact of a Wind Surfing intervention on Math achievement of second graders. For example, examine this paragraph generated after I requested a qualitative study instead:

"As this study focuses on qualitative exploration, the quantitative results will not be the primary focus. However, to provide a broader context for the qualitative findings, basic descriptive statistics of math achievement scores may be reported for both the windsurfing instruction group and the control group. These scores will be collected through pre- and post-intervention math assessments administered to all participants. The quantitative results will be used to complement and contextualize the qualitative findings, providing a broader perspective on students' math achievement in relation to their windsurfing experiences."

I am sure that generative AI will create an increase of papers submitted for publication. To prevent science from being overwhelmed and suspicious we may need to write new rules and accelerate existing trends.

1. Demand researchers pre-register their research.

2. Ask that each paper submitted will include a statement about the use of generative AI and will include the transcripts of their use.

3. Create ethical standards for AI use in scientific publishing AND teach about it in graduate schools.

4. Create reviewing mechanisms that allow easy tracking of citations to the source.

Let's Chat about GPT event at UNL