This workshop explores the intersection of Learning Analytics (LA) and Generative Artificial Intelligence (GenAI) to support and empower teachers in their pedagogical and administrative practices. With the increasing availability of educational data and the rapid advancements in AI technologies, there is a growing need to develop human-centered frameworks and tools that enhance, rather than replace, the professional agency of teachers.
The workshop focuses on how Evidence-Based Decision-Making (EBDM) - grounded in the principles of LA - can inform classroom practices, curriculum design, student engagement strategies, and differentiated instruction. Simultaneously, it examines how GenAI can support teachers by offloading routine tasks such as lesson planning, design of learning activities, preparing quizzes, summarizing student responses, designing formative assessments, and suggesting differentiated instructional strategies. This allows teachers to focus on high-value aspects of teaching, such as mentoring, empathy-driven feedback, and designing inclusive learning environments.
A central pillar of the workshop is the "human-in-the-loop" approach, which ensures that AI-enhanced solutions keep teachers at the core of decision-making processes. This paradigm emphasizes the importance of teacher agency, context-awareness, and value-sensitivity in designing AI-supported tools. Rather than promoting automation for its own sake, the workshop seeks to explore ways AI can act as a collaborative partner, extending the teacher's capabilities while aligning with their pedagogical beliefs, ethical principles, and cultural contexts.
We are particularly interested in approaches that are socio-technical in nature, taking into account not only the technical affordances of GenAI and LA but also the institutional, emotional, and interpersonal dynamics of teaching. The workshop invites reflections on how educational systems can develop trustworthy, transparent, and adaptable AI interventions that empower rather than marginalize teachers, especially in diverse and resource-constrained settings.
By bringing together interdisciplinary voices from research, practice, and design, the workshop aims to advance a shared vision of teacher-AI partnerships that are purposeful, equitable, and evidence-informed, shaping the future of education with care, creativity, and responsibility.
We invite researchers, practitioners, designers, and policy experts to submit short papers, position papers, and work-in-progress reports to our workshop that explores how teachers can be meaningfully supported through a combination of LA and GenAI technologies.
This workshop aims to stimulate interdisciplinary conversations and practical innovations around the following broad themes:
Learning Analytics to support teachers’ evidence-based decision-making
Designing GenAI-powered tools to augment teacher workflows (e.g., lesson planning, feedback, assessment)
Human-in-the-loop approaches in AI-supported teaching and learning
Ethical, responsible, and context-aware use of AI in educational environments
Case studies of AI-enabled teaching support systems in diverse contexts
Teacher agency, trust, and perceptions of AI-driven decision-making
Frameworks and metrics to evaluate the impact of AI on teacher empowerment
Intersections of policy, practice, and technology in teacher-AI collaborations
We especially encourage contributions that are reflective, critical, or exploratory in nature, including preliminary research, practical deployments, and conceptual frameworks.
Submission Guidelines:
Length: 4 - 6 pages (excluding references)
Deadline: TBD
Submission Portal: TBD
Review Process: Peer-reviewed by the workshop organizing committee
Please send your submission to shashi.shankar@ahduni.edu.in
Expected Outcomes:
The workshop aims to build a community of researchers and practitioners committed to advancing teacher empowerment in an AI-enhanced educational future. Selected papers will be presented during the workshop, followed by thematic group discussions and collaborative roadmap planning. Outcomes may include a post-workshop report, collaborative publication plans, and the expansion of an ongoing effort in building a special interest group.
Join us as we reimagine the role of teachers - not as passive recipients of AI tools, but as active agents in shaping how technology transforms teaching and learning.
Shashi Kant Shankar, Ahmedabad University, shashi.shankar@ahduni.edu.in
Jainendra Shukla, IIIT Delhi, jainendra@iiitd.ac.in
Chandrashekar Lakshminarayanan, IIT Madras, chandresh@iiti.ac.in
Ramkumar Rajendran, IIT Bombay, ramkumar.rajendran@iitb.ac.in
Kshitij Sharma, NTNU, Norway
Rwitajit Majumdar, University of Kumamoto, Japan
Aditi Kothiyal, IIT Gandhinagar, India
Tanmay Sinha, NTU, Singapore
Shitanshu Mishra, MGIEP, UNESCO