Smart questioning, answering, and clarifying to support adaptive learning

Purpose Generative artificial intelligence (GAI) is increasingly integrated into higher education; however, many AI-based systems function primarily as reactive answer generators rather than adaptive instructional supports. This study develops and evaluates smart questioning, answering, and clarifying (SmartQAC), a pedagogically grounded AI-supported learning system that operationalizes structured dialogic scaffolding through questioning, answering, and clarification mechanisms to support adaptive learning. Design/methodology/approach SmartQAC integrates retrieval-augmented generation, prompt-routing strategies, and structured dialogic interactions to promote metacognitive engagement and conceptual development. A quasi-experimental study involving 52 undergraduate students was conducted within a database course as an illustrative domain application. Mixed-method analyses were performed using pre-test and post-test assessments, interaction logs, questionnaires, and interviews to examine learning achievement, learning behaviors, and learner perceptions. Findings Results indicate that students using SmartQAC achieved significantly higher learning achievement than those in the control group. Interaction behaviors within the SmartQAC environment were positively associated with students' learning achievement. Furthermore, participants reported positive perceptions regarding instructional usefulness, autonomy support, engagement, and pedagogical value. Originality/value This study demonstrates how dialogic instructional principles can be operationalized within a retrieval-augmented large language model system to support adaptive learning. The findings provide empirical evidence for the effectiveness of integrating questioning, answering, and clarification mechanisms into AI-supported learning environments and offer practical design implications for future adaptive educational systems.