Disciplinary thinking and AI perception: a case study of undergraduate students’ evaluation of an AI music quiz

Introduction Artificial intelligence in the form of Large Language Models is increasingly being incorporated into music education, yet much of the existing literature concentrates on composition, performance, assessment, creativity, and teacher adoption rather than music humanities education. This article examines student responses to Mystic Melodies, a gamified adaptive-difficulty AI quiz used in a compulsory first-year musicology unit at the Sydney Conservatorium of Music. Methods Students attended a one-hour lecture and then completed the quiz based on the lecture content during a one-hour tutorial. The quiz used short answer questions, adaptive difficulty, hints, immediate feedback, explanatory teaching points, and a quest narrative in which correct responses generated gifts while incorrect responses withheld them. Results Thirty-four post-activity surveys comprising five-point Likert items and open-ended questions were received. The quantitative data show that students felt positive about how well they understood the topic, but were divided about all other aspects of their app experience. The qualitative data suggests an explanation for this pattern. Several critical comments point to concerns about humanities learning, expert teaching, independent thinking, meaningful feedback, and the limits of correct-incorrect quiz logic. Discussion This intervention highlighted a design problem: in music humanities education, AI-supported activities may be accepted as useful review or feedback tools, but they risk resistance when students perceive them as replacing expert-led, interpretive, or dialogic forms of learning.