Teacher education for artificial intelligence literacy through a self-determination theory perspective

Synthesis: Chiu, Bali, Tondeur, Howard, and Chan (2026) apply self-determination theory (SDT) to investigate how need-supportive professional development (PD) impacts teachers' AI literacy, attitudes, anxiety, and engagement in online professional learning communities (PLCs). Using a sequential mixed-methods approach with 382 secondary school teachers, they found that need-supportive PD enhances AI literacy and fosters sustained behavioral engagement in PLCs, with AI literacy emerging as the key cognitive driver of sustained participation. Qualitative analysis identified nine specific design strategies for PD that satisfy teachers' needs for autonomy, competence, and relatedness — bridging the often-overlooked connection between isolated PD and PLCs to support the scaling and sustainability of teacher AI learning.

Key Findings

Need-supportive PD positively predicts needs satisfaction (β = .77, p < .001) and behavioral engagement in PLCs (β = .47, p < .001) — a well-designed PD can spark ongoing, collaborative learning.

Needs satisfaction significantly predicts AI literacy (β = .52), attitude (β = .68), and reduces AI anxiety (β = −.66) — PD content alone is insufficient; how it is delivered (whether it supports psychological needs) determines its effectiveness in changing teachers' AI knowledge, feelings, and beliefs.

AI literacy is the key cognitive gateway to sustained engagement: among the AI learning outcomes, only AI literacy had a significant direct effect on behavioral engagement in PLCs (β = .39, p < .001); attitude (p = .72) and anxiety (p = .18) were non-significant — teachers need a foundation of ability before actively engaging in peer-to-peer learning.

The theoretical contribution is a validated motivational pathway: PD needs support → needs satisfaction → cognitive (AI literacy) and affective (attitude, anxiety) learning → sustained behavioral engagement, with AI literacy as the primary cognitive driver.

Nine design strategies were identified: four autonomy-supportive (elective AI topic selection, collaborative AI resource development/co-design, individualised AI integration targets, non-mandatory task completion), two competence-supportive (expert-peer feedback on AI learning designs, public sharing of AI teaching artefacts), and three relatedness-supportive (cross-disciplinary AI design teams, mentorship, mission-alignment of AI learning).

Study Design & Method

This sequential mixed-methods study examined 382 secondary school teachers engaged in AI-focused professional development. Quantitative phase: participants completed validated SDT-based questionnaires (perceived PD support, autonomy, relatedness, competence) plus measures of AI attitude, AI anxiety, and AI literacy (a 30-item multiple-choice test drawn from the AI4future item bank), and behavioral engagement in online PLCs was objectively measured by posts, replies, reads, sharing, and collaborations. Data were analyzed with confirmatory factor analysis (CFA) and structural equation modeling (SEM), with good model fit (χ²/df = 1.43, RMSEA = .03, CFI = .99). Qualitative phase: deductive content analysis of interviews using SDT as the analytic framework identified the nine design strategies, with two independent raters and a moderator (inter-rater reliability = 0.88).

Implications for AI in Education

The study provides an evidence-based blueprint for designing effective, sustainable AI-focused teacher PD, directly relevant to Faculty Development, Professional Training, and Teacher AI Competency. It argues that PD providers and universities should structure AI training to explicitly incorporate the nine need-supportive strategies, prioritize building foundational AI Literacy (the sole direct predictor of PLC engagement), and position PD as a catalyst for self-sustaining learning communities rather than a one-time endpoint. Theoretically, it extends SDT to the AI context, showing that for highly technical domains like AI, knowledge acquisition is the gateway to collaborative participation — a finding that connects to Motivation, Self Determination Theory, and K 12 teacher education. It bridges the PD/PLC divide, offering a motivational pathway for scaling and sustaining teacher AI learning.

Limitations

The study's context is secondary school teachers (largely in Chinese/Hong Kong and East Asian settings), bounding generalizability to other regions and K-12 contexts. The authors acknowledge that sustaining a need-supportive culture within PLCs after the structured program ends is challenging, that systemic constraints (rigid curricula, limited resources, top-down mandates) can undermine need-supportive PD, and that the strategies may overlook the mediating role of contextual factors like teacher prior AI experience or entrenched school culture. The AI literacy test was a specific objective measure developed for the AI4future project.

Connected Concepts

AI Literacy

Teacher AI Competency

Faculty Development

Professional Training

Self Determination Theory

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Citation

Chiu, T. K. F., Bali, S., Tondeur, J., Howard, S., & Chan, K. K. H. (2026). Teacher education for artificial intelligence literacy through a self-determination theory perspective. European Journal of Teacher Education.