Trustworthy AI for Education: Generative AI, Learning Analytics, and Equity

Introduction

Generative AI (GAI) and Machine Learning (ML) are rapidly changing how learning is produced, supported, and evaluated. In particular, ubiquitous AI assistance challenges traditional outcome-based assessment and academic integrity practices, while expanding opportunities for personalization, feedback, and early identification of learning needs. This workshop convenes educators, researchers, academic integrity leaders, and educational technologists to examine
GAI+ML readiness, responsible use, and equity impacts—focusing on process-aware assessment and learning analytics that move beyond the final artifact to evidence of learning. The workshop blends peer-reviewed research presentations, tool and dataset demonstrations, and interactive breakouts where participants (i) compare integrity and assessment redesign strategies under GAI, (ii) evaluate ML-driven analytics and predictive models for validity, fairness, and interpretability, and (iii) co-design privacy-preserving governance patterns for adoption at course and institutional levels. AssessME will be used as a primary case study and demonstration platform for process-based learning traces and analytics, alongside other approaches and tools discussed by participants.

Submission Deadline: 20 February 2026

Notification Deadline: 1 March 2026

Camera Ready Deadline: 15 March 2026

Topics

1) GAI Readiness in Teaching and Learning

Readiness Models: Frameworks for instructors, students, and programs adopting GAI-assisted workflows in programming and creative media production.
Baseline Diagnostics: Assessing digital literacy, GAI policy awareness, and support needs in technical and design education.
Meaningful Learning Outcomes: Designing curriculum goals that remain relevant when code and high-fidelity visual assets can be generated instantaneously.
Creative Workflow Readiness: Preparing students for the shift from manual asset
creation to prompt engineering and AI-assisted prototyping.

2) Ethical and Responsible Use of AI in Education

Academic Integrity: Defining authorship, acceptable-use boundaries, and transparency requirements for code, text, and visual media.
Translating trustworthy-AI principles into GenEd-ready classroom routines (AI literacy, ethical authorship, and process-based assessment) that work across writing-, humanities-, and communication-focused courses.
Ethical Trace Collection: Addressing surveillance concerns, proportionality, and student agency when collecting learning traces.
Bias and Fairness: Evaluating performance prediction and automated content generation for algorithmic bias, particularly in visual representation.
IP and Attribution: Establishing standards for disclosing AI usage in professional portfolios and creative artifacts.

3) Transforming Teaching and Learning Practices

Formative Feedback: Integrating analytics tools into coaching and feedback loops for both developers and designers.
GAI-Enhanced Instructional Design: Using AI for scaffolding, debugging practice, reflective design prompts, and rapid prototyping.
Inclusive Support: Using predictive models to identify struggling learners early and provide differentiated interventions.
Iterative Design Traces: Tracking the evolution of a design concept or codebase to value the learning process over the final render.

4) Rethinking Assessment and Research

Process-Based Assessment: Developing rubrics and evidence models that evaluate learning traces beyond the final code artifact or design file.
Analytics and Predictive Modeling: Utilizing IDE and design software interaction traces to understand student engagement and iteration.
Research Validation: Methods for cross-institution replication, dataset sharing, and anonymization in education research.
Interpretable ML: Developing explainable indicators and instructor-in-the-loop reviews to ensure AI-driven assessments are transparent.
Equity-Aware Evaluation: Using fairness metrics and subgroup performance checks to mitigate bias in learning analytics.

5) Educational Leadership, Policy, and Quality Assurance

Institutional Governance: Managing consent, data retention, access control, and transparency reporting at the organizational level.
Quality Standards: Aligning GAI-assisted workflows with accreditation and demonstrating evidence of meeting learning outcomes.
Change Management: Strategies for faculty development, student communication, and creating adoption roadmaps for new media and tech programs.

Organizers

Alan Mutka (RIT Croatia)
Domagoj Tolić (RIT Croatia)

Workshop publication

Accepted and presented papers will be submitted for publishing alongside the main conference proceedings as a sub-section/chapter. Paper formats should, therefore, correspond to the templates of the publisher of the main conference.