Generation Z Prefers Teacher-Assisted over Artificial Intelligence-Assisted Mathematics Learning: A Self-Determination Theory Analysis

Abstract

Purposes:  The rapid integration of artificial intelligence (AI) in education has reinforced techno-optimistic assumptions that algorithmic efficiency can support increasingly autonomous learning. This study examined Generation Z students’ preferences for AI-assisted and teacher-assisted mathematics learning through the lens of Self-Determination Theory (SDT). Method: A quantitative within-subject comparative design was employed to collect data from 481 students in South Sulawesi, Indonesia. Participants evaluated both learning conditions using a 4-point Likert-scale questionnaire covering six dimensions: Concept Clarity, Feedback, Motivation, Learning Mode, Trust, and Comfort in Asking Questions. Paired-sample t-tests were used to compare students’ evaluations across the two conditions. Findings: Students rated teacher-assisted learning significantly higher than AI-assisted learning across all six dimensions (p < .001). The largest mean differences were observed in Concept Clarity and Trust, while the smallest occurred in Comfort in Asking Questions. Teacher-assisted learning was also rated more favorably in Learning Mode, despite the flexibility commonly associated with AI-based learning. Although AI-assisted learning received moderately positive evaluations, the overall pattern suggests that students placed greater value on the conceptual and relational support associated with teacher assistance. Significance: The findings suggest that technological flexibility alone may not be associated with stronger student preference for mathematics learning. Within this context, AI may be better positioned as a complementary instructional resource rather than a replacement for teacher assistance. Because the study relied on self-reported perceptions, the findings should not be interpreted as evidence of comparative instructional effectiveness.