Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument).¶

Claim · All claims
Evidence · 1 study (2 entries) · theoretical r2 · q1
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Evidence quality · No study behind this claim is a peer-reviewed article with a DOI: it rests on an ERIC document (a report, conference paper or thesis). It is kept because 3 design pages cite it; treat it as a lead to check, not an established finding.

Subclaims¶

q1 i? The paper argues that teachers' beliefs in their ability to successfully integrate AI technologies shape their attitudes and intentions toward those technologies, making expectancy a critical factor in motivation to use AI. → Yurt 2024 q1 i? The paper argues that past experiences, technology knowledge and skills, professional development opportunities and a supportive school environment can shape teachers' expectations about using AI. → Yurt 2024 (2)

Evidence¶

Yurt 2024¶

Yurt, E. (2024). Theoretical Examination of Teachers' Motivations for Using Artificial Intelligence Applications in Education: Expectancy-Value Theory Perspective. In M. S. Ozturk, A. Kaban, & M. Unal (Eds.), Proceedings of ICSES 2024-- International Conference on Studies in Education and Social Sciences (pp. 149-158), Istanbul, Turkiye. ISTES. https://eric.ed.gov/?id=ED673212

q1 · i? · theoretical · r2

Theoretical argument in the paper's expectancy section, supported by citation of prior work rather than new data: it states that "Expectancy is a critical factor directly influencing teachers' motivation to use AI applications", attributing the attitudes-and-intentions link to Ranellucci et al. (2020).

"Expectancy is a critical factor directly influencing teachers' motivation to use AI applications. Teachers' beliefs in their ability to successfully integrate AI technologies shape their attitudes and intentions towards these technologies (Ranellucci et al., 2020)."

Yurt 2024 (2)¶

Yurt, E. (2024). Theoretical Examination of Teachers' Motivations for Using Artificial Intelligence Applications in Education: Expectancy-Value Theory Perspective. In M. S. Ozturk, A. Kaban, & M. Unal (Eds.), Proceedings of ICSES 2024-- International Conference on Studies in Education and Social Sciences (pp. 149-158), Istanbul, Turkiye. ISTES. https://eric.ed.gov/?id=ED673212

q1 · i? · theoretical · r2

Theoretical argument in the same section, offered as an example with no data: the paper lists that "professional development opportunities, and a supportive school environment can shape their expectations" about using AI technologies.

"teachers' past experiences, knowledge and skills in using technology, professional development opportunities, and a supportive school environment can shape their expectations about using AI technologies."

Studies bearing on this claim¶

Do teachers who are more confident they can use AI well (expectancy) hold more favourable attitudes towards it and use it more? This table gathers the studies behind this claim and the 8 narrower claims that bear on it: 6 distinct studies (4 associational, 1 theoretical, 1 review), q1–q3, 1 of 6 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Related Claims¶

• Teachers who believe AI use will contribute to their professional success and identity (attainment value) may be more motivated to use it (theoretical argument). — related
• Teachers who perceive the challenges and barriers of using AI as high (cost) may be less motivated to use it (theoretical argument). — related
• Teachers who find using AI enjoyable, engaging and satisfying (intrinsic/interest value) may be more motivated to use it (theoretical argument). — related
• Teachers who believe AI will help them reach professional goals and improve students' learning outcomes (utility value) may be more motivated to use it (theoretical argument). — related
• Instruction should present the learning task as engaging and meaningful and promote positive expectations of success, because effort requires that learners value the task and believe they can succeed — a broader claim this one bears on
• Learners' task-specific self-efficacy beliefs predict whether they initiate, persist through, and recover from difficulty on academic tasks. — related
• Learners invest more effort in tasks they perceive as interesting, personally important, or useful to their future goals. — a broader claim this one bears on
• Teachers hold conflicting attitudes about whether gameful assessments support students’ intrinsic motivation and desire to learn — related
• Faculty attitudes toward AI fall into four profiles—optimistic, critical, critically reflective, and neutral—and optimistic faculty with high AI self-efficacy are more likely to adopt AI — related
• Effortful learning strategies posed a student motivation challenge at d.tech, which teachers addressed by explaining the underlying science directly to students — related
• AI applications support prompt engineering, professional skill development, technology acceptance, and active participation in science and chemistry education — related
• Teachers combined high concern about education's future with belief that change requires active effort and that AI is associated with progress — related
• AI applications in STEM education foster positive affective perception, including attitude, interest, and motivation — related
• The teacher behaviours most predictive of engagement change do not neatly fit within any single motivational theory, supporting theoretical integration — related
• A teacher's positive initial response to blended delivery was shaped by experience, TELL beliefs and personality — related
• Motivation, affect, and learner beliefs shape whether AI evidence is accepted, resisted, or used in singing training — related
• AI knowledge and attitudes toward AI did not significantly predict teachers' AI integration ability — reports the opposite
• Teachers' digital competence is a critical factor influencing both AI tool adoption and its pedagogical effectiveness — related
• Teacher characteristics from the will-skill-tool model — data literacy, availability of data technologies, and positive beliefs — predict teachers' pedagogical use of digital data, with data literacy the strongest predictor — related
• Teacher confidence strongly predicts favorable attitudes toward AI adoption, while the direct effect of support on attitudes is not significant — a narrower finding that bears on this claim
• Teacher confidence fully mediates the relationship between institutional support and attitudes toward AI adoption — related
• A gap exists between theory and practice: teachers do not optimally apply the effective teaching principles, and teacher beliefs and contextual factors account for this gap — related