Applications of Generative AI to Support Teaching and Learning in Higher Education
July 22, 2025 - 9:00-12:30 CEST - Palermo, Italy & Virtually
This half-day workshop is part of the 2025 Artificial Intelligence in Education Conference
July 22, 2025 - 9:00-12:30 CEST - Palermo, Italy & Virtually
This half-day workshop is part of the 2025 Artificial Intelligence in Education Conference
Over the past three years, interest in generative AI (GenAI) at the AIED conference has grown exponentially, reflecting the emergence of innovative approaches to enhance teaching and learning. LLMs in particular open new opportunities to address ongoing challenges in this space and advance higher education practices. While many of these interventions benefit education broadly, there remains a pressing need to examine their practical implications specifically within universities. Factors such as learner maturity, class sizes, subject areas, and instructor experience all play pivotal roles in shaping how GenAI can be most effectively deployed in this setting.
This half-day workshop therefore centers on the application of GenAI for supporting teaching and student learning in higher education. We aim to showcase how to develop and use GenAI interventions that empower instructors and students alike. Participants will learn strategies to successfully implement GenAI in their courses, engage in hands-on activities with relevant tools, and explore the broader opportunities that ongoing advancements in GenAI afford. We invite researchers, educators, and practitioners eager to expand and refine teaching and learning practices at the university level to join us. It will focus on exploring the opportunities and challenges of integrating GenAI in supporting teaching and learning in higher education teaching.
AI Course Assistants – The use of AI-powered assistants or chatbots to support instructors and students in courses. For example, AI teaching assistants can answer routine questions on discussion forums, as demonstrated by Georgia Tech’s Jill Watson (an IBM Watson-based TA that handled forum Q&A in an online class). Such AI assistants can scale support for large classes, freeing human instructors for complex tasks. Notably, early trials showed students often didn’t realize a responder was an AI, indicating these systems can effectively mimic helpful human support.
Feedback Generation and Automated Assessment – Techniques for leveraging GenAI to provide personalized feedback on student work. Large language models (LLMs) like Anthropic's Claude Sonnet, Google's Gemini 2.0, and OpenAI's GPT-4o can be used to generate formative feedback on essays, projects, or coding assignments. Recent research has explored using LLMs to write human-like feedback that is informative and encouraging for students. This theme examines how AI-generated feedback might improve learning by giving students timely, tailored guidance, as well as how instructors can curate or edit AI feedback to ensure quality. We will discuss scenarios such as AI tools that summarize a student's work and offer suggestions, helping instructors handle large classes or provide 24/7 support.
AI-Integrated Learning Activities – Design of learning activities and assignments that meaningfully incorporate AI tools. Rather than banning AI, some educators now require students to use AI for certain tasks to enhance learning. We will share use-cases like AI-assisted writing exercises, using AI to generate quiz questions or study guides, and creative projects where students collaborate with AI (e.g. using an AI as a brainstorming partner or a coding helper). This theme also covers how AI can personalize learning experiences by adapting content or practice questions to individual student needs, aligning with the goal of more inclusive and personalized education.
Challenges in Adoption – A critical look at the hurdles institutions and instructors face when incorporating GenAI into teaching. A primary concern is the risk of students using AI to circumvent learning (e.g. getting answers or writing from AI without understanding). We will discuss academic integrity issues and strategies to prevent misuse, such as designing “AI-proof” assessments or teaching AI literacy so students use GenAI responsibly. Another challenge is faculty preparedness and training – not all educators are comfortable with AI, and many need guidance on effective integration. Technical limitations of current GenAI will be addressed as well, including the well-known tendency of LLMs to “hallucinate” (make up plausible-sounding but incorrect information), and how this impacts trust and accuracy in educational use. Participants will collaboratively consider solutions to these challenges.
Ethical and Inclusiveness Considerations – Throughout all topics, we will emphasize the ethical implications and the goal of equitable, inclusive education. This includes ensuring AI tools are fair and unbiased, respect student privacy, and are accessible to diverse student populations, which is a known challenge in this space. The workshop will explore questions like: How do we mitigate AI biases that could disadvantage or offend certain groups? How can GenAI be used to reduce barriers for marginalized students (for instance, providing extra tutoring support at no cost, or offering translations and adaptive content)? We will invoke guidelines on responsible AI in education, aligning with AIED 2025’s theme of using AI to empower teachers and students for an equitable future. Case studies of ethical AI usage and policies (such as disclosure of AI use, data transparency, and obtaining consent for AI analysis of student data) will be discussed. Ultimately, this theme is about ensuring that GenAI is a positive catalyst for inclusion and ethics in the classroom rather than a source of new inequalities.
While no submission is required to participate in the workshop, we invite submissions of research, work-in-progress, and position papers, targeting 3 to 5 pages not counting references (please use one of these templates Latex, Word). Submissions are not archival.
The submissions should address at least one of the themes above. They should contain mostly novel work, however there can be overlap between the submission and work submitted elsewhere (such as summaries, describing the process of the work, and focusing on the learnersourcing aspect). Each of the submissions will be reviewed by multiple members of the Program Committee and should be double-blind (do not include author names in the submission).
Submission URL: https://easychair.org/my/conference?conf=genaihed25
Submission Deadline: June 1, 2025 - 11:59 pm AoE time
Submission Notifications: June 18, 2024
While we encourage participant submissions, we will have plenty of activities and other ways to contribute during the workshop itself. Presentation of the submissions will only be a portion of the workshop, as we plan to keep participants engaged and contributing through other ways during the workshop, along with plans to have multiple artifacts be created during the workshop.
The workshop will follow an interactive and structured format designed to foster engagement and knowledge sharing among participants. The half-day event will include a combination of presentations, discussions, and hands-on group activities. This will be followed by a panel discussion on challenges, ethics, and implementation, addressing critical issues such as responsible AI use and academic integrity. The final session will involve an interactive group activity, where participants collaboratively design AI-powered interventions for education. The workshop will conclude with closing remarks and a discussion on future collaborations, ensuring that participants leave with actionable insights and connections for continued engagement. A tentative schedule is provided below.
09:00 - 09:20: Introduction and Welcome
Overview of workshop objectives and introduction of organizers and attendees.
09:20 - 10:20: Session 1: Presentations on Cutting-Edge Research
Selected short paper presentations highlighting recent research on AI course assistants, AI-generated feedback, and AI-integrated learning activities.
Each talk will focus on the design, implementation, and empirical evaluation of AI-based educational tools.
Q&A and discussion after each presentation.
10:20 - 10:35: Break
10:35 - 11:20: Session 2: Challenges, Ethics, and Practical Implementation
Lightning talks and panel discussion featuring experts on:
Ensuring responsible and ethical AI usage in classrooms.
Institutional challenges in adopting AI tools for education.
Addressing student misuse, academic integrity, and faculty training.
Panel Q&A with audience participation.
11:20 - 12:05: Session 3: Interactive Group Activity – Designing AI-Integrated Learning Strategies
Participants will be divided into small groups to discuss and prototype AI-based interventions for their courses or research projects.
Groups will explore:
How to design effective AI-driven learning activities.
Best practices for AI-generated feedback and student engagement.
Identifying potential risks and mitigation strategies.
Each group will present their ideas in a brief share-out session.
12:05 - 12:20: Closing Remarks and Next Steps
Summary of key takeaways, opportunities for future collaborations, and call for continued engagement.
*All times are local (Palermo, Sicily).
Location: Palermo, Sicily.
Virtual (Zoom): The Zoom link will posted here the morning of the workshop
Waivers for virtual attendance will be provided on an as-needs basis, please contact kizilcec@cornell.edu if you’d like to attend virtually, but need financial assistance.
The workshop will be held in a hybrid format, offering opportunities for both in-person and remote participation.
Contact: kizilcec@cornell.edu, ylh8@cornell.edu
Ryan Baker, University of Pennsylvania
Christopher Brooks, University of Michigan
Kevyn Collins-Thompson, University of Michigan
Jadon Geathers, Cornell University
Yann Hicke, Cornell University
Rene Kizilcec, Cornell University
Steven Moore, Carnegie Mellon University
Narges Norouzi, University of California, Berkeley
Jiliang Tang, Michigan State University
Bo Wu, Colorado School of Mines