Teachers' trust in AI ‐powered educational technology and a professional development program to improve it
Open accessAbstract
Evidence from various domains underlines the critical role that human factors, and especially trust, play in adopting technology by practitioners. In the case of Artificial Intelligence (AI) powered tools, the issue is even more complex due to practitioners' AI‐specific misconceptions, myths and fears (e.g., mass unemployment and privacy violations). In recent years, AI has been incorporated increasingly into K‐12 education. However, little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology (AI‐EdTech). This paper sheds light on teachers' trust in AI‐EdTech and presents effective professional development strategies to increase teachers' trust and willingness to apply AI‐EdTech in their classrooms. Our experiments with K‐12 science teachers were conducted around their interactions with a specific AI‐powered assessment tool (termed AI‐Grader) using both synthetic and real data. The results indicate that presenting teachers with some explanations of (i) how AI makes decisions, particularly compared to the human experts, and (ii) how AI can complement and give additional strengths to teachers, rather than replacing them, can reduce teachers' concerns and improve their trust in AI‐EdTech. The contribution of this research is threefold. First, it emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. Second, it presents a teacher professional development program (PDP), as well as the discourse analysis of teachers who completed it. Third, based on the results observed, it presents clear suggestions for future PDPs aiming to improve teachers' trust in AI‐EdTech. Practitioner notes: What is already known about this topic Human factors, and especially trust, play a critical role in practitioners' adoption of technology. In recent years, AI has been incorporated increasingly into K‐12 education. Little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology. What this paper adds This research emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. It presents a teacher professional development program (PDP) to increase teachers' trust in AI‐EdTech, as well as the discourse analysis of teachers who completed it. It presents clear suggestions for future PDPs aiming at improving teachers' trust in AI‐EdTech. Implications for practice and/or policy Pre‐ and in‐service teacher education programs that aim to increase teachers' trust in AI‐EdTech should include a section providing teachers with a basic understanding of AI. PDPs aimed to increase teachers' trust in AI‐EdTech should focus on concrete pedagogical tasks and specific AI‐powered tools that are considered by teachers as helpful and worth the effort to learn. AI‐EdTech should not restrict teachers to follow specific pedagogical scenarios, but rather provide teachers with the freedom to design and implement various types of pedagogies that meet their preferences, students' needs, and classroom reality. Teacher agency is key to gaining their trust. AI‐EdTech should allow teachers to review, modify, and if necessary, override AI‐based recommendations before they are sent to students.
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Many experienced teachers in the focal OPLC utilised GenAI at a high level, in contrast to some recent findings that experienced teachers resist GenAI use.
“in contrast to some recent findings that experienced teachers resist GenAI use (e.g., Nazaretsky etal. 2022), many in our focal OPLC utilised it at a high level.”
From Knowledge (Co-)Construction Among Artificial Intelligence, Novice Teachers, and Experienced Teachers in an Online Professional Learning Community (2025) · cites this paper as Nazaretsky et al. 2022
The Introduction and Literature Review says K–12 teachers might avoid incorporating AI-powered educational technology into everyday teaching, which supports the general point about resistance. But the study concerns an AI assessment tool, not GenAI, and its Methodology records teachers’ experience without establishing that experienced teachers specifically resist its use. The focal OPLC’s usage is the citing authors’ own finding, not a claim attributed to this source. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredModern AI systems can accurately transcribe speech, detect affective states from facial expressions, estimate student attention from gaze direction, and classify discourse types from linguistic features.
“Modern AI systems can accurately transcribe speech, detect affective states from facial expressions, estimate student attention from gaze direction, and classify discourse types from linguistic features-capabilities that, when combined, offer unprecedented insight into the structure and quality of classroom interaction [2], [3].”
From AI-Assisted Classroom Interaction Analysis for Enhancing Teaching Effectiveness in Higher Education (2026) · cites this paper as [2]
The Data collection and discourse analysis section describes researchers classifying teachers’ utterances; it does not describe AI classifying classroom discourse. Searches for speech transcription, facial affect detection, gaze-based attention estimates, and classroom-interaction analysis found no passage addressing the claimed capabilities. Page numbers were not available in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredPerceived trust addresses the significant security-related barriers and ethical concerns that teachers may have with AI.
“Meanwhile, perceived trust addresses the significant security-related barriers and ethical concerns that teachers may have with AI [5].”
Lack of trust stemming from an insufficient understanding of AI operations contributes to resistance among teachers towards the integration of AI-based tools into their instructional practices.
“The lack of trust stemming from an insufficient understanding of AI operations contributes to resistance among teachers towards the integration of AI-based tools into their instructional practices [5,56].”
Many studies have demonstrated that when educators trust AI-based tools, they are more likely to recognize and utilize these tools' capabilities in enhancing teaching and learning outcomes.
“Many studies have demonstrated that when educators trust AI-based tools, they are more likely to recognize and utilize these tools' capabilities in enhancing teaching and learning outcomes [5,58].”
An AI-literate teacher's trust in GenAI's consistent performance determines whether they perceive it as genuinely useful.
“An AI-literate teacher might understand the potential of GenAI, but it is their trust in the tool's consistent performance that will determine if they perceive it as genuinely useful [5,56].”
Some studies use focus group interviews, statistical methods, or a combination of these methodologies.
“Some studies use focus group interviews [75], statistical methods [76], or a combination of these methodologies [81].”
From In search of artificial intelligence (AI) literacy in teacher education: A scoping review (2024) · cites this paper as [81]
Some studies cover several of the outlined topics.
“Some of the studies[18,81] cover several of the outlined topics.”
From In search of artificial intelligence (AI) literacy in teacher education: A scoping review (2024) · cites this paper as [81]
AI literacy is often discussed in combination with other literacies, such as media literacy and digital literacy and data literacy, and frequently used interchangeably with concepts such as AI readiness, AI education, and AI thinking.
“AI literacy is also often discussed in combination with other liter acies, such as media literacy and digital literacy [70] and data literacy [81,20], and frequently used interchangeably with concepts such as AI readiness [45]; Wa [41], AI education [69,72], and AI thinking [12].”
From In search of artificial intelligence (AI) literacy in teacher education: A scoping review (2024) · cites this paper as [81]
A notable number of papers discuss the need for teachers to comprehend the principles underpinning various AI EdTech to effectively integrate them into their teaching practices.
“a notable number of papers discuss the need for teachers to comprehend the principles un derpinning various AI EdTech to effectively integrate them into their teaching practices [41,45,81,84,91,95].”
From In search of artificial intelligence (AI) literacy in teacher education: A scoping review (2024) · cites this paper as [81]
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- Authors
- Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G.
- Affiliations
- Weizmann Institute of Science (IL)
- University College London (GB)
- Corresponding author
- Nazaretsky, Tanya
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2022-05-09
- Volume
- 53
- Issue
- 4
- Pages
- 914-931
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W4229456833
- Cited by
- 589
- References
- 30
- Citations by year
- 2026: 206 · 2025: 197 · 2024: 138 · 2023: 41 · 2022: 7
- FWCI
- 94.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- British Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Azrieli Foundation
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Education and Technology Integration
- Social Sciences › Education › Teacher Education and Leadership Studies
- Keywords
- teacher professional development, K-12 science teachers, explainable AI, teacher attitudes toward AI, AI adoption in education, synthetic data, pre-service and in-service teacher education
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