Development of an Explainable Artificial Intelligence Framework for Academic Integrity, Inclusive Learning, and Quality Assurance in Digital Higher Education

Abstract

Abstract: The rapid adoption of digital higher education has transformed teaching, learning, and assessment by increasing accessibility and flexibility. However, it has also introduced challenges related to academic integrity, equitable access to learning opportunities, and effective quality assurance. Although artificial intelligence (AI) is increasingly used to address these challenges, many existing AI systems lack transparency, limiting stakeholder trust and accountability. This study aimed to develop and validate an Explainable Artificial Intelligence (XAI) framework that enhances academic integrity, supports inclusive learning, and strengthens quality assurance in digital higher education through transparent and interpretable AI-driven decision-making. A convergent mixed-methods research design was employed. Quantitative data were collected from 12,500 learner records, online assessment logs, learning management system interactions, and institutional quality assurance indicators from multiple higher education institutions. Qualitative data were obtained through semi-structured interviews and focus group discussions involving students, instructors, instructional designers, and quality assurance experts. Explainable machine learning algorithms and fairness assessment techniques were integrated into the proposed framework to detect academic misconduct, identify at-risk learners, evaluate educational quality, and generate interpretable recommendations. The proposed framework achieved an accuracy of 95.1%, precision of 94.3%, recall of 93.8%, an F1-score of 94.0%, and an Area Under the Curve (AUC) of 0.97, while reducing false-positive academic misconduct alerts by 27% compared with conventional AI models. Qualitative findings demonstrated that explainable AI significantly improved stakeholder trust, perceived fairness, transparency, and confidence in AI-supported educational decision-making. The developed XAI framework provides a transparent, ethical, and scalable solution for improving academic integrity, promoting inclusive learning, and strengthening quality assurance in digital higher education. The framework contributes to trustworthy AI adoption and supports evidence-based institutional decision-making, thereby advancing sustainable digital transformation in higher education. Keywords: Academic Integrity; Inclusive Learning; Quality Assurance; Digital Higher Education.; Keywords:Explainable Artificial Intelligence (XAI)