XAI-Profile: Explainable AI for Transparent Learner Profiling in Digital Education

Abstract

Trust-aware digital learning systems are essential for informed decision-making by educational stakeholders, as the growing complexity of digital learning environments increases the need for transparent and explainable learner profiles. While digital learning traces are crucial for identifying learner behaviors and categories, extracting meaningful profiles from large and complex data remains challenging due to bias, misinterpretation, and limited stakeholder guidance. Moreover, traditional learning analytics tools struggle to transform low-level traces into human-interpretable insights. In response to these shortcomings, we introduce a novel framework that exploits explainable artificial intelligence (XAI) to convert digital learning traces into meaningful and behavioral information. By integrating explainable machine learning techniques with human-centered design process, our framework connects fine-grained learning traces to high-level pedagogical interpretations. XAI-Profile is structured into three main components: (1) goal-driven requirements to decompose stakeholders’ goals into measurable subgoals linked to learner data, (2) visual analytics design to interpret learner profiles through transparent analytical artifacts, and (3) trust flow to support hypothesis testing and actionable, context-aware insights. Experimental evaluation in the écri+ project demonstrates that our framework generates interpretable learner profiles and validates pedagogical hypotheses. Educator-in-the-loop assessment confirms improved transparency and trust through interactive visual analytics. This work bridges XAI outputs with pedagogical insight, establishing a scalable, human-centered foundation for learning analytics. 

Received: 9 April 2025 | Revised: 4 January 2026 | Accepted: 18 June 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest related to this work. 

Data Availability Statement

The data that support the findings of this study are openly available in GitHub at https://github.com/ouared14/XAI-Profile.

Author Contribution Statement

Abdelkader Ouared: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Madeth May: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Claudine Piau-Toffolon: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision, Project administration. Nicolas Dugué: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Visualization, Supervision.