From Signals to Action: Explainable AI for Engagement-Responsive Instructional Support in Digital Higher Education

Andino Maseleno , Meinhaj Hussain , Aygul Z. Ibatova

Artificial intelligence is increasingly used to monitor learning processes in higher education; however, many analytics pipelines still terminate at prediction and provide limited support for instructional action. The research establishes an explainable artificial intelligence framework which utilizes digital learning environment behavioral data and contextual information to create customized instructional support solutions. The analysis uses xAPIEdu-Data dataset which contains 480 records to build engagement index and create support profiles and predict multiclass performance through rule based action allocation. The study tests three classification models using stratified cross validation. The study selects Random Forest as the most effective system because it delivers superior results across all tests. The selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out sample. The analysis shows that student absence and composite engagement index andgender and student guardian relationship and support profile and digital resource access arethe most important factors that determine student performance. The final decision layer manages student assignment to instructional support plans which contain attendance-first intervention and adaptive engagement support and family-engagement reinforcement and structured progression coaching and challenge-and-extend pathways. The study develops an analytical framework which connects explainable artificial intelligence to digital higher education instructional decision support systems.

Doi: https://doi.org/10.54216/IJAIET.050101

Vol. 5 Issue. 1 PP. 01–12, (2026)