Secondary students in AI learning fall into three distinct SDT-based motivational profiles — Disengaged, Developing, and Self-Determined — at both pre- and post-test¶

Claim · All claims
Evidence · 1 study · associational r2 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 Latent profile analysis identified three qualitatively distinct need-satisfaction profiles at both time points, differing strongly on all five motivational indicators (η²p = 0.55–0.59). → Liang 2026

Evidence¶

Liang 2026¶

Liang, S., Yau, K. W., Meng, H., Chiu, T. K. F., King, I., Yam, Y., & Chai, C. S. (2026). From disengaged to self-determined: A latent transition analysis of students' AI learning motivation. Education and Information Technologies. https://doi.org/10.1007/s10639-026-13998-y

q2 · i3 · associational · r2

MANOVA validation of the three latent profiles among 2,086 matched secondary students showed significant multivariate differences across profiles on all five indicators, with η²p = 0.55, 0.57, 0.56, 0.58, and 0.59 for AICF, AIKG, AISG, AIET, and AIBI respectively (F(2, 2085) values as printed). Profile sizes were Disengaged 7.3% (N = 152), Developing 62.6% (N = 1307), Self-Determined 30.2% (N = 630).

"F(2, 2085) 1289.07 1361.96 1299.96 1422.32 1525.74 η 2 p 0.55 0.57 0.56 0.58 0.59"

Discussion¶

Related Claims¶

• Students who transitioned into or remained in the Self-Determined profile showed the greatest improvements in AI literacy — a narrower finding that bears on this claim
• During a year-long AI curriculum, most students maintained or moved upward in motivational profile, with substantial upward transitions out of the Disengaged profile — related
• Greater prior AI learning experience and female gender predicted upward motivational profile transitions — related
• Latent profile analysis of pre-service teachers' CT skills identifies three profiles (Novice, Developing, Proficient), revealing heterogeneous, non-linear skill acquisition — related
• Exploratory K-means clustering identifies four heterogeneous student AI-user profiles differing in experience, competence, usefulness, trust, and control, but the solution's optimality and stability are not established — related
• Four distinct LLM learner profiles emerge from latent class analysis of learning contexts, tasks, and devices — related