ARTIFICIAL INTELLIGENCE IN LEARNING DESIGN:
A REVIEW OF INNOVATIONS IN DIGITAL EDUCATION

ABSTRACT

Artificial Intelligence (AI) by shifting to a structural element of how learning is designed, delivered, and assessed, is becoming a central force in education more than a supportive tool. This article reviews studies published between 2020 and 2025 examining the innovations AI has introduced into digital learning design. The analysis is organized into four themes: human-centered AI, adaptive and personalized learning, AI-enhanced assessment and feedback, and inclusive and accessible learning design. Findings highlight that stressing the accountability of human-centered AI, transparency, and human agency, its application remains uneven in education. There are concerns about equity, algorithmic bias, and data privacy, although adaptive and personalized learning present strong potential to improve engagement, flexibility, and learner autonomy. AI-assisted assessment and feedback expand opportunities for timely and personalized support. Nevertheless, challenges around academic integrity and institutional policy remain. Within this scope, inclusivity and fairness emerge as key priorities, with evidence that AI can reduce barriers for marginalized learners, although risks of reinforcing exclusion remain without strong safeguards. Overall, the literature suggests that AI in education faces fragmentation more than a lack of innovation. This review contributes by synthesizing current research as a structured overview by offering insights for educators, researchers, and policymakers striving to integrate AI in learning design responsibly and equitably.