Research Article
From Voluntary Practice Towards Actionable Learning Analytics: Architecture, Feature-Level Use, and Design Implications for an AI-Supported Learning Platform
@ARTICLE{10.4108/dtip.14898, author={Radka Stolarikov\^{a} and Aneta Vadovičov\^{a} and Monika Dobi\^{a}šov\^{a} and Petr Val\^{a}šek and Jiř\^{\i} Votava}, title={From Voluntary Practice Towards Actionable Learning Analytics: Architecture, Feature-Level Use, and Design Implications for an AI-Supported Learning Platform}, journal={EAI Endorsed Transactions on Digital Transformation of Industrial Processes}, volume={2}, number={2}, publisher={EAI}, journal_a={DTIP}, year={2026}, month={10}, keywords={AI-supported learning, learning analytics, higher education, event logging, formative feedback, teacher-facing dashboards}, doi={10.4108/dtip.14898} }- Radka Stolariková
Aneta Vadovičová
Monika Dobiášová
Petr Valášek
Jiří Votava
Year: 2026
From Voluntary Practice Towards Actionable Learning Analytics: Architecture, Feature-Level Use, and Design Implications for an AI-Supported Learning Platform
DTIP
EAI
DOI: 10.4108/dtip.14898
Abstract
INTRODUCTION: Voluntary use of AI-supported learning platforms may differ across features, while aggregate logs cannot show whether teachers act on analytics. OBJECTIVES: To evaluate AI Sensei’s architecture, logged activity across platform features, self-reported feature use and student feedback. METHODS: An observational pilot analysed logs for 205 students and an anonymous questionnaire (N = 100). Training quizzes and feedback were programmed; chat used generative AI. RESULTS: All 205 students submitted training quizzes and self-check tests (1,999 and 481 submissions, respectively); 164 (80.0%) sent at least one chat message. In the separate anonymous questionnaire, 91% selected quizzes/tests and 17% the chat assistant; 94% agreed that combining teacher support with AI Sensei was more beneficial than teaching without the platform. CONCLUSION: Structured practice merits refinement; teacher-facing analytics require evaluation for actionability.
Copyright © 2026 Radka Stolariková et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.