WORKSHOP OBJECTIVES AND INTENDED OUTCOMES

The primary objective of this half-day workshop is to bring together researchers, practitioners, and policymakers to critically examine how LA and GenAI can be combined to genuinely support teachers’ evidence-based decision-making (Prieto et al., 2019). Rather than positioning AI as a replacement for teacher judgment, the workshop emphasizes a human-in-the-loop approach that foregrounds professional agency, contextual awareness, and ethical responsibility (Rodríguez-Triana et al., 2018; Shankar et al., 2025). Through this framing, the workshop seeks to advance a nuanced understanding of how data and AI technologies can empower teachers to design, adapt, and sustain effective learning environments (Yingsoon et al., 2025).

Within this broad aim, the workshop has several intended outcomes. First, it will surface diverse frameworks, case studies, and conceptual models that illustrate how teacher agency can be maintained and strengthened when analytics and AI are integrated into everyday practice (Shankar et al., 2025; Mouta et al., 2025). Second, it will provide participants with a collaborative space to co-create design principles and evaluation strategies for responsible teacher–AI partnerships (Lin et al., 2021).

ORGANIZATIONAL DETAILS OF THE PROPOSED EVENT

The workshop is designed as a half-day (3.5 hours) interactive symposium that blends scholarly

exchange with collaborative engagement. The format is presented below.

1. Opening Talk (30 Minutes) - A short keynote-style introduction that establishes the central theme of empowering teachers through Learning Analytics (LA) and Generative AI (GenAI). This session is to provide the conceptual foundation and situate the discussion within broader debates on evidence-based decision-making (Prieto et al., 2019) and teacher agency in AI-supported education (Hong et al., 2025).

2. Presentation of Accepted Submissions (60 minutes) - Approximately six presenters will share short papers, position pieces, or work-in-progress reports (10 minutes each). These presentations will foster structured scholarly exchange while creating opportunities for interaction between presenters and participants.

3. Interactive Thematic Discussions (45 minutes) - Participants will engage in “birds of a feather” style group discussions. Thematic foci will include ethics, trust, transparency, and the preservation of teacher agency in AI-driven educational environments.

4. Collaborative Design Sprint (45 minutes) - Small groups will co-create frameworks, workflows, or evaluation approaches for integrating LA and GenAI into teacher practice. This activity emphasizes hands-on design and co-production of knowledge.

5. Synthesis Plenary (30 minutes) - Each group will share highlights, followed by a collective identification of priority areas for future research, design, and policy.

CALL FOR WORKSHOP PAPERS

We invite researchers to contribute to the From Data to Decisions: Exploring Teacher-AI Partnerships for Equitable and Responsible Learning Analytics (FDD) workshop, which will take place in person on April 28th, 2026, as part of the LAK26 conference. We welcome submissions on the following topics, but not limited to:

• Human-centered and socio-technical perspectives on Learning Analytics and GenAI

• Preserving teacher agency in data-driven educational environments

• Design frameworks for responsible and explainable AI integration

• Ethics, trust, transparency, and accountability in teacher–AI collaboration

• Data-informed instructional design and assessment strategies

• Case studies, prototypes, or classroom interventions using LA and GenAI

• Policy, infrastructure, and capacity-building for equitable AI in education

We particularly encourage submissions that blend theoretical insights with practical applications, co-design methods, or cross-disciplinary perspectives to advance responsible teacher–AI partnerships.

SUBMISSION GUIDELINES

The organizers welcome two categories of submissions: short empirical papers highlighting ongoing research, and short discussion papers aimed at sparking dialogue around pivotal issues and challenges.

Submissions should be between 5 and 7 pages in length and formatted according to the 1-column CEUR-ART template. All manuscripts must be anonymized for double-blind review prior to submission via the EasyChair system.

Each submission will undergo a double-blind peer-review process, conducted by members of the organizing committee and contributing authors.

Accepted papers will be published in the FDD 2026 Workshop Proceedings, which will be submitted to CEUR-WS.org for open-access online publication.

Contact e-mail: shashi2y22@gmail.com

IMPORTANT DATES

Submission opens: October 21st, 2025

Deadline for abstract submissions (optional, for getting feedback about the scope from the organizers): preferable before November 20th, 2025

Deadline for manuscript submissions: December 4th, 2025

Notification of acceptance: December 19th, 2025

Workshop date: April 28th, 2026

WORKSHOP ORGANIZERS

• Shashi Kant Shankar, Ahmedabad University, India

• Ramkumar Rajendran, Indian Institute of Technology, Bombay, India

• Rwitajit Majumdar, Kumamoto University, Japan

• Shitanshu Mishra, MGIEP, UNESCO, India

• Ashwin T. S., Vanderbilt University, USA

• Kshitij Sharma, NTNU, Norway

References

• Lin, P., & Van Brummelen, J. (2021, May). Engaging teachers to co-design integrated AI curriculum for K-12 classrooms. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-12).

• Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2025). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies, 30(3), 3343-3387.

• Prieto, L. P., Rodríguez-Triana, M. J., Martínez-Maldonado, R., Dimitriadis, Y., & Gašević, D. (2019). Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level. Australasian Journal of Educational Technology, 35(4).

• Rodríguez-Triana, M. J., Prieto, L. P., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2018, March). The teacher in the loop: Customizing multimodal learning analytics for blended learning. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 417-426).

• Shankar, S. K., Pothancheri, G., Sasi, D., & Mishra, S. (2025). Bringing teachers in the loop: Exploring perspectives on integrating generative AI in technology-enhanced learning. International Journal of Artificial Intelligence in Education, 35(1), 155-180.

• Yingsoon, G. Y., Zhang, S., & Chua, N. A. (2025). Empowering Educators in the Era of Next-Generation AI: Redefining the Teacher's Role in an AI-Enhanced Learning Environment. In Teachers' Roles and Perspectives on AI Integration in Schools (pp. 1-30). IGI Global Scientific Publishing.