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.