Proposer
Workshop Code
Please use the following code when submitting your paper to this Workshop: W1-TEACHING
Scope and Aims
As AI tools such as ChatGPT, Copilot, and education-specific agents proliferate, institutions are grappling with how to integrate them constructively, rather than reactively. Moreover, these systems aim to offer students personalised, on-demand learning support, such as answering questions, recommending resources, and simulating dialogue with expert tutors. This workshop focuses on how AI can augment teaching, assessment, and feedback practices in higher education, while ensuring pedagogical soundness, academic integrity, and student engagement. Additionally, it covers the design, development, and evaluation of AI-driven tutoring systems and learning companions for higher education.
The workshop aims to:
- Showcase emerging AI applications that support lecturers and assessors (e.g., feedback generation, rubric alignment, formative assessment tools).
- Explore the pedagogical impact and evidence base for AI in teaching and learning.
- Address academic policy concerns such as plagiarism, AI-authorship, and fairness in assessment.
- Facilitate collaboration between AI researchers, educators, EdTech developers, and instructional designers.
- Advancing research on adaptive learning agents powered by LLMs, knowledge graphs, and dialogue systems.
- Evaluating the pedagogical effectiveness and ethical considerations of deploying AI tutors at scale.
- Creating guidelines and toolkits for designing transparent, reliable, and culturally sensitive AI companions.
This is significant as AI challenges traditional teaching models and opens opportunities for rethinking feedback loops, assessment design, and teaching support at scale. Furthermore, higher education increasingly serves diverse, global cohorts with varied learning needs, and AI-powered companions can address gaps in access to support, but must be aligned with curriculum goals, sensitive to student needs, and free from bias.
Key Themes
- AI for automated feedback, marking support, and formative assessment
- AI-supported scaffolding and personalised tutoring agents
- Use of large language models (LLMs) to enhance learning analytics
- Designing assessment for AI-augmented contexts (e.g., open-AI assessments)
- Institutional policy, ethics, and detection of inappropriate AI use
- Case studies on teacher workload reduction through AI
- Design principles for intelligent tutoring systems (ITS) using AI and NLP
- AI-generated explanations, feedback, and Socratic dialogue
- Alignment of AI tutoring with curricula and learning outcomes
- Multilingual and inclusive AI tutor systems
- Risk of misinformation, hallucinations, and dependency on AI
- Measurement of learning gain, engagement, and trust
Intended Outcomes
- A whitepaper or report synthesizing AI assessment challenges and strategies
- Draft AI-in-Assessment policy templates for universities
- A network of AI-in-education researchers and practitioner partners
- Prototyping roadmap for responsible AI tutor development
- Annotated benchmark dataset for tutor-agent dialogues in education
- Cross-institutional research agenda on evaluation and pedagogy for AI tutoring