Teachers who perceive the challenges and barriers of using AI as high (cost) may be less motivated to use it (theoretical argument).¶

Claim · All claims
Evidence · 1 study (3 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 2 design pages cite it; treat it as a lead to check, not an established finding.

Subclaims¶

q1 i? The paper argues that when teachers perceive the challenges and barriers of using AI technologies as high, they may be less motivated to use them. → Yurt 2024 q1 i? The paper argues that the time and effort needed to learn and integrate AI, and a lack of adequate resources and support, can raise teachers' cost perceptions and reduce motivation. → Yurt 2024 (2) q1 i? The paper argues that concerns about the reliability and effectiveness of AI technologies can increase teachers' cost perceptions. → Yurt 2024 (3)

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 cost section, with no new data: "When teachers perceive the challenges and barriers of using AI technologies as high, they may be less motivated" to use them.

"Teachers' cost perceptions regarding the use of AI applications directly influence their motivation to use these technologies. When teachers perceive the challenges and barriers of using AI technologies as high, they may be less motivated to use these technologies."

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 cost section citing prior work (Cheng et al., 2020; Chan & Zhou, 2023; Ranellucci et al., 2020), no new data: "the lack of adequate resources and support can increase teachers' cost perceptions".

"Additionally, the lack of adequate resources and support can increase teachers' cost perceptions, reducing their motivation to use AI technologies (Cheng et al., 2020; Chan & Zhou, 2023; Ranellucci et al., 2020)."

Yurt 2024 (3)¶

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 about risk and uncertainty in the cost section, with no data: teachers "may be concerned about the reliability and effectiveness of AI technologies", raising perceived cost.

"teachers may be concerned about the reliability and effectiveness of AI technologies, which can increase their cost perceptions."

Studies bearing on this claim¶

Do perceived costs and risks of AI (effort, output quality, threats to students' thinking) hold teachers back from using it? This table gathers the studies behind this claim and the 4 narrower claims that bear on it: 5 distinct studies (3 associational, 2 theoretical), q1–q2, 0 of 5 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¶

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