The NARST 2026 Annual Meeting invites educators, researchers, and communities to reimagine science education as a space of possibility, hope, and joy. In urgent times, we seek radical approaches that transcend boundaries and foster equity, care, and creativity. This event will allow to explore how science teaching and learning can center lived experiences, embrace play, and cultivate trust—building futures where all can flourish.  

The GENUIS Center will participate in the conference, being held in Seattle, April 19–22, 2026, to envision transformative technology that connects AI to science education. We enthusiastically invite you to visit us at one (or all!) of the sessions below: 

NARST Book Talk 1: Artificial Intelligence for STEM Education Research: Advanced Methods and Applications 

Presider: Xiaoming Zhai and Gyeonggeon Lee 

Discussant: Kent Crippen 

This talk introduces a newly published book from Springer, Artificial Intelligence for STEM Education Research: Advanced Methods and Applications. The book features a rich collection of chapters that explore how AI can be harnessed as research methodologies to enhance educational outcomes via applications such as adaptive learning systems, automatic scoring, explainable AI, and intelligent tutoring. The editors (Xiaoming Zhai – University of Georgia; and Gyeonggeon Lee – Nanyang Technological University) invited contributions from experts in AI-integrated STEM education research. Authored by leading experts, Artificial Intelligence for STEM Education Research: Advanced Methods and Applications is designed for researchers, educators, and graduate students who are interested in applying AI-driven methods to their research and work. This session will present an overview of the book, introduced by editors and chapter authors. The practical examples, case studies, and methodological details presented throughout the volume make it a valuable resource for those seeking to push the boundaries of traditional research methods and embrace the possibilities offered by AI. Participants will gain in-depth insights into cutting-edge AI applications and their potential to revolutionize STEM education research. This session is ideal for anyone looking to understand and apply AI technology for STEM education research. 

Session Topics and Panelists: 

Title Panelist 
Artificial Intelligence for STEM education research (overview) Xiaoming Zhai  
Using Artificial Intelligence for Automatic Scoring Ehsan Latif  
Automated Assessment Generation Matthew Nyaaba 
Adaptive Learning in STEM Education Research Yizhu Gao 
Computerized Adaptive Testing for STEM Learning Shiyu Wang 
Computer Vision in STEM Education Research Gyeonggeon Lee 
Knowledge Tracing Liang Zhang 
Intelligent Tutor for STEM Learning Ehsan Latif 
Recommender Systems for STEM Education Research Qiaoyu Tan 
Sentiment Analysis for STEM Learning Shuchen Guo 
Explainable AI in STEM Education Research Ninghao Liu 

NARST Book Talk 2: Advancing AI in Science Education (AASE): Envisioning Responsible and Ethical Practice 

Presider: Xiaoming Zhai and Kent Crippen 

Discussant: Joseph Krajcik 

This session explores how artificial intelligence (AI) can be thoughtfully and responsibly integrated into science education to promote high-quality learning opportunities for all students. Supported by NSF (2332964), the RAISE co-chairs convened an international committee of experts in AI and science education to collaboratively envision the future of AI in K–12 contexts. The committee’s early findings highlight both the transformative potential of AI—such as generative tools like ChatGPT—and the ethical considerations required to ensure its use upholds fairness, academic integrity, and accessibility. Presenters will introduce a conceptual framework that aligns 3-dimensional learning with responsible and ethical principles for AI integration. This session invites educators, researchers, policymakers, and technology developers to engage in dialogue around opportunities, challenges, and pathways for shaping a trustworthy and ethically grounded future of AI in science education. 

Session Topics and Panelists 

Title Panelist 
The Landscape of AI in Science Education: What is Changing and How to Respond Xiaoming Zhai, Kent Crippen 
AI in Contemporary Science Practice: Implications for AI-Integrated Science Education Ross Nehm, Marcus Kubsch 
Responsible and Ethical Principles for the Practice of AI-Supported Science Education Kent Crippen, Xiaoming Zhai, Okhee Lee 
SECTION II: EDUCATIONAL GOALS 
AI-Enhanced Science Literacy: Science Literacy Plus Xiufeng Liu, Jennifer Tripp 
AI Transformations of Science Education Goals to Prepare Students as Lifelong Investigators Marcia Linn 
SECTION III: EDUCATIONAL PROCEDURES 
Science Teaching and Learning with Artificial Intelligence Knut Neumann, Xiaoming Zhai, Marcus Kubsch 
Capitalizing on AI to Create Critical Entry Points to Support Science Teacher Learning and the Work of Science Teaching Jamie N. Mikeska, Natalie King, Yizhu Gao 
SECTION IV: LEARNING MATERIALS 
Transforming Science Learning Materials in the Era of Artificial Intelligence Xiaoming Zhai, Kent Crippen 
SECTION V: ASSESSMENT 
Addressing Challenges of Science Assessment with Artificial Intelligence Lei Liu, Kevin Haudek, Xiaoming Zhai, Knut Neumann 
AI-Based Science Assessment: Potential Pitfalls and Responsible and Ethical Practice Gyeonggeon Lee, Xiaoming Zhai, Okhee Lee 
SECTION VI: EDUCATIONAL OUTCOMES 
Preparing Students for Living in an AI-Infused World: Revisiting the Outcomes of Science Education Marcus Kubsch, Paul Hur, Knut Neumann, Juan-Carlos Aguilar 
SECTION VII: CONCLUSION 
Charting the Future of AI-Supported Science Education: A Human-Centered Vision Xiaoming Zhai, Kent Crippen 

Research in Artificial Intelligence-involved Science Education: RAISE RIG Poster Session 

The NARST 2026 Annual Meeting invites educators, researchers, and communities to reimagine science education as a space of possibility, hope, and joy. In urgent times, we seek radical approaches that transcend boundaries and foster equity, care, and creativity. This event will allow to explore how science teaching and learning can center lived experiences, embrace play, and cultivate trust—building futures where all can flourish.  

The GENUIS Center will participate in the conference, being held in Seattle, April 19–22, 2026, to envision transformative technology that connects AI to science education. We enthusiastically invite you to visit us at one (or all!) of the sessions below: 

Presider: Dr. Xiaoming Zhai, University of Georgia 

Discussant: Dr. Kent Crippen, University of Florida 

Poster Presentations 

Cluster 1: AI in Curriculum and Student Learning 

  1. Leveraging Integrated Curriculum and Educational Robotics to Develop AI Literacy in Upper Elementary Grades -Erdoga Kaya, Ezgi Yesilyurt, Refika Turgut, Hasan Deniz, Dilara Kara Zorluoglu, Elif Adibelli-Sahin 
    • Focuses on multilingual learners using robotics and block-based programming (SNAP!) to explore AI concepts.
    • Aligns with AI4K12’s Five Big Ideas and embeds unplugged and plugged activities 
  2. Design Principles for Human–AI Interaction: Problem-Solving Experiences of Two Physics Students with ChatGPT’s Study Mode – Fatma Caner, Mehmet Aydeniz 
    • Comparative case study of proficient vs. struggling physics students. 
    • Examines how AI scaffolds or undermines metacognitive engagement. 
  3. What Is AI Interest? Distinct Construct, Part of STEM Interest, or Hype? -Arne Bewersdorff, Xiaoming Zhai, Claudia Nerdel 
    • Explores whether “AI interest” is a standalone construct or a subset of STEM interest. 
    • Uses surveys with 150–200 university students. 
  4. Embodied AI Literacy: A Case Study of ML Education in an Informal STEM Setting-Manav Sharma, Ji Shen 
    • Introduces wearable devices (BBC micro:bit) to teach machine learning through body movement. 
    • Conducted with underprivileged youth at a community tennis academy. 

Cluster 2: AI in Scientific Practices and Inquiry 

  1. First-of-its-Kind Application of GenAI for Molecular Design: Secondary Science Teachers Engineer Novel Sweeteners -Minji Yun, Kent Crippen, Charles Xie, Dylan Bulseco
    •  Teachers use a ChatGPT-enabled platform (AIMS) to design novel molecules. 
    • Highlights how GenAI can make authentic molecular design accessible. 
  2. A Framework for Designing Science and Engineering Practice Activities for an AI-Involved Learning Environment -Jongchan Park, Xin Xia, Nejla Nuruk, Xiaoming Zhai 
    • Proposes reconceptualizing NGSS-aligned activities for AI-mediated classrooms. 
    • Emphasizes interactivity, multimodality, and compatibility. 
  3. Supporting Middle School Students’ Scientific Investigations with an AI LabAgent: Insights from a LabAgent Case Study -Xin Xia, Nejla Yuruk, Lihua Tan, Jongchan Park, Xiaoming Zhai 
    • Embeds an AI-driven agent in simulations of contact forces. 
    • Examines scaffolding functions and students’ perceptions. 
  4. Exploring the Use of AI to Increase Data Inquiry Accessibility -Daniel Damelin, Lynn Stephens, Geoff Freed 
    • Introduces DAVAI, an AI-supported voice-based interface for data exploration. 
    • Prioritizes accessibility for blind/low-vision students. 

Cluster 3: AI for Assessment and Feedback 

  1. Open-Source AI for Automatic Feedback on Students’ Written Responses to Physics Tasks -André Meyer, Gunnar Friege 
    • Uses OpenChat 3.6 (open-source LLM) to provide feedback. 
    • Compares performance against ChatGPT and human raters. 
  2. Integrating Generative AI in STEM Assessment: When to Forbid, Scaffold, or Require Collaboration -Yizhu Gao, Min Li, Zhongzhou Chen, Xiaoming Zhai 
    • Proposes a five-level framework aligning AI use with Bloom’s taxonomy. 
    • Offers practical guidance for assessment design. 
  3. AI-Mediated Feedback for Teacher PD Sessions -Noel Kuriakos, Christine Hirst Bernhardt 
    • Applies AI-assisted transcript analysis to teacher professional development sessions. 
    • Provides personalized, unbiased, and granular feedback. 

Cluster 4: AI in Teacher Learning and Professional Development 

  1. Using GenAI Digital Teaching Simulations in Science to Support Teacher Learning -Shreyashi Halder, Jamie N. Mikeska, Tricia Maxwell 
    • Develops GenAI-powered teaching simulations where teachers practice eliciting student thinking. 
    • Evaluates authenticity and accuracy of AI-student interactions. 
  2. Exploring the Potential of Applying LLMs for Teaching Dialogic Science Communication Skills  -Elad Yacobson, Ofra Amir, Ayelet Baram-Tsabari 
    • Introduces “Prodigy,” an LLM-based simulator for training scientists in dialogic communication. 
    • Pilot study with 37 graduate students shows improvement in communication skills. 
  3. Design Principles of Customized Science Simulations Using Generative Artificial Intelligence -Gyeonggeon Lee 
    • Investigates how GenAI can empower teachers to design their own simulations. 
    • Extracts design principles grounded in pedagogy, content, and standards. 

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