Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust
Open accessAbstract
This study delves into the factors that drive teachers’ adoption of generative artificial intelligence (GenAI) technologies in higher education. Anchored by the technology acceptance model (TAM), the research expands its inquiry by integrating the constructs of intelligent technological pedagogical content knowledge (TPACK), AI literacy, and perceived trust. Data were gathered from a sample of 237 university teachers through a structured questionnaire. The study employed structural equation modeling (SEM) to determine the relationships among the constructs. The results revealed that both AI literacy and perceived ease were the most influential factors affecting teachers’ acceptance of GenAI. Notably, intelligent TPACK and perceived trust were found to be pivotal mediators in this relationship. The findings underscore the importance of fostering AI literacy and adapting intelligent TPACK frameworks to better equip educators in the age of AI. Furthermore, there is a clear need for targeted professional development initiatives focusing on practical training that enhances AI literacy. These programs should provide hands-on experience with GenAI tools, boosting educators’ confidence and ability to integrate them into their teaching practices.
AI snapshot
Written from the paper’s full text.
TL;DR
This study models university teachers’ acceptance of generative AI by extending TAM with AI literacy, intelligent TPACK, and perceived trust. It finds perceived ease of use and AI literacy are the strongest direct drivers of GenAI acceptance, with intelligent TPACK and perceived trust acting mainly as mediators.
Key findings
- Perceived ease of use (β = 0.48, t = 4.53) and AI literacy (β = 0.35, t = 3.83) directly affected GenAI acceptance, while perceived usefulness (β = 0.16, t = 1.35) and intelligent TPACK (β = 0.04, t = 0.40) had no significant direct effect.
- Intelligent TPACK had a significant indirect effect on GenAI acceptance through perceived ease of use (indirect β = 0.23, t = 2.36; total β = 0.33), and AI literacy had significant indirect effects on perceived usefulness via perceived trust (indirect β = 0.12; total β = 0.25) and on perceived ease of use via intelligent TPACK (indirect β = 0.31; total β = 0.39).
- AI literacy directly affected intelligent TPACK (β = 0.64) and perceived trust (β = 0.30); perceived trust affected perceived usefulness (β = 0.39); perceived ease of use affected perceived usefulness (β = 0.28).
- The model explained GenAI acceptance with R² = 0.65 and Q² = 0.38.
Methods
- A structured questionnaire was administered to 237 university teachers in April–May 2023; approximately 600 were invited, yielding a 40% response rate. Participants were lecturers, assistant professors, and professors with at least some prior encounter with generative AI.
- The sample was 77.6% female; 74.6% were lecturers or assistant professors, and 72.1% were aged 25–44.
- Data were analyzed with partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4.0, following a two-stage measurement and structural model process. Six constructs were measured with 31 items on a five-point Likert scale, adapted from prior instruments.
Limitations
- The paper states the study focused on individual-level factors; future research should examine organizational factors, cognitive load, and pedagogical integration challenges.
- The paper notes reliance on self-reported measures may introduce bias and does not capture real-time GenAI use or integration; the cross-sectional design limits understanding of adoption over time, and cultural/regional differences were outside the study’s scope.
- Evident from the text: the sample came from one university context and was predominantly female, which may limit generalizability.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 42 cited sources checked · 0 supported · 0 flagged
- Findingown data
The R2 for GenAI acceptance was 0.65, indicating high and outstanding predictive ability.
“The R 2 for GenAI acceptance (0.65) showed a high and outstanding predictive ability, suggesting the model's ability to anticipate teachers' acceptance of GenAI.”
Quote in the paperSubstantial claim - Findingown data
The total effect of AI literacy on perceived ease of use was statistically significant (β = 0.39, t = 3.49).
“The total effect of AI literacy on perceived ease of use proved to be statistically significant (H8: β = 0.39, t = 3.49**).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingreasoning
The total effect of AI literacy on perceived usefulness is β = 0.25.
“The total impact (total β = 0.25) demonstrates that while AI literacy does not directly drive perceived usefulness, its indirect effects-mediated by perceived trust-combine to create a positive overall perception of GenAI's utility.”
Quote in the paperSubstantial claim - Findingown data
Intelligent TPACK has no direct effect on teachers' acceptance of GenAI (β = 0.04, t = 0.40).
“However, perceived usefulness (H1: β = 0.16, t = 1.35) and intelligent TPACK (H6: β = 0.04, t = 0.40) showed no direct effect on teachers' acceptance of GenAI.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
The total effect of AI literacy on perceived ease of use is β = 0.39.
“The total effect being significant (total β = 0.39) further highlights the substantial overall impact of AI literacy on perceived ease of use when mediated through enhanced TPACK capabilities.”
Quote in the paperSubstantial claim
- Findingown data
Perceived ease of use has a positive relationship with perceived usefulness (β = 0.28).
“The support for H3 (β = 0.28) indicates a positive relationship between perceived ease of use and perceived usefulness.”
- Also stated in: § 7.1. TAM Relationships · hedged
“This suggests that when GenAI tools were easier to use, they were also perceived as more useful.”
Quote in the paperSubstantial claim - Also stated in: § 7.1. TAM Relationships · hedged
- Findingown data
AI literacy has a positive direct effect on GenAI acceptance (β = 0.35).
“H10 was significantly supported (β = 0.35), showing that AI literacy directly influenced GenAI acceptance.”
Quote in the paperSubstantial claim - Findingown data
Intelligent TPACK directly influences AI literacy (β = 0.64, t = 8.55).
“Furthermore, the results indicated that AI literacy was directly influenced by both intelligent TPACK (H9: β = 0.64, t = 8.55**) and perceived trust (H11: β = 0.30, t = 2.44*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Perceived trust directly influences AI literacy (β = 0.30, t = 2.44).
“Furthermore, the results indicated that AI literacy was directly influenced by both intelligent TPACK (H9: β = 0.64, t = 8.55**) and perceived trust (H11: β = 0.30, t = 2.44*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Intelligent TPACK has a significant indirect effect on GenAI acceptance through the mediation of perceived ease of use (β = 0.23, t = 2.36).
“Although the results in Table 4 reveal an insignificant direct effect of intelligent TPACK on GenAI acceptance (H6: β = 0.04, t = 0.40), they show a significant indirect effect of intelligent TPACK on GenAI acceptance through the mediation of perceived ease of use (H6: β = 0.23, t = 2.36*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Perceived ease of use directly affects perceived usefulness (β = 0.28, t = 2.23).
“In addition, the results showed that perceived usefulness was directly affected by both perceived ease of use (H3: β = 0.28, t = 2.23*) and perceived trust (H12: β = 0.39, t = 3.38**), supporting H3 and H12.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Intelligent TPACK has a significant indirect effect on GenAI acceptance through perceived ease of use (indirect β = 0.23).
“However, the indirect path through the perceived ease of use was significant (indirect β = 0.23), indicating that intelligent TPACK can facilitate GenAI adoption by making the technology easier to use.”
Quote in the paperSubstantial claim - Findingown data
Perceived ease of use has a significant positive effect on GenAI acceptance.
“The results revealed a complex landscape in which perceived ease of use significantly influenced both the acceptance of GenAI and its perceived usefulness, underscoring the importance of userfriendly design in GenAI technologies.”
Quote in the paperSubstantial claim - Findingown data
AI literacy has a positive direct effect on intelligent TPACK (β = 0.64).
“The strong support for H9 (β = 0.64) indicates a direct and robust relationship between AI literacy and intelligent TPACK.”
Quote in the paperSubstantial claim - Findingown data
AI literacy has a significant indirect influence on perceived ease of use through the intermediary role of intelligent TPACK (β = 0.31, t = 2.54).
“However, there was a significant indirect influence of AI literacy on perceived ease of use, facilitated through the intermediary role of intelligent TPACK (H8: β = 0.31, t = 2.54*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
AI literacy has a significant indirect effect on perceived usefulness through perceived trust (indirect β = 0.12).
“However, the significant indirect path through perceived trust (indirect β = 0.12) reveals an important mediating role of trust in the technology.”
Quote in the paperSubstantial claim - Findingown data
AI literacy has a positive direct effect on perceived trust (β = 0.30).
“With a significant result (β = 0.30), H11 was supported, confirming that AI literacy enhances perceived trust in GenAI tools.”
Quote in the paperSubstantial claim - Findingown data
AI literacy directly affects GenAI acceptance (β = 0.35, t = 3.83).
“Perceived ease of use (H2: β = 0.48, t = 4.53**) and AI literacy (H10: β = 0.35, t = 3.83**) were found to be the two factors directly affecting GenAI's acceptance”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
AI literacy has an indirect effect on perceived usefulness mediated by perceived trust (β = 0.12, t = 2.10).
“However, AI literacy was found to have a direct impact on perceived usefulness, facilitated by the mediating role of perceived trust (H7: β = 0.12, t = 2.10*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Perceived ease of use directly affects GenAI acceptance (β = 0.48, t = 4.53).
“Perceived ease of use (H2: β = 0.48, t = 4.53**) and AI literacy (H10: β = 0.35, t = 3.83**) were found to be the two factors directly affecting GenAI's acceptance”
- Also stated in: § 7.1. TAM Relationships · boosted
“H2's result was significant (β = 0.48), strongly supporting the idea that perceived ease of use positively influences GenAI adoption.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Also stated in: § 7.1. TAM Relationships · boosted
- Findingown data
AI literacy has no direct effect on perceived usefulness (β = -0.13, t = 0.91).
“However, AI literacy had no direct effect on either perceived usefulness (H7: β = -0.13,t = 0.91) or perceived ease of use (H8: β = 0.08, t = 0.49), which resulted in the rejection of H7 and H8.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Intelligent TPACK has a positive effect on perceived ease of use (β = 0.49).
“With a significant result (β = 0.49), H5 supports the idea that higher levels of intelligent TPACK improve the perceived ease of use of GenAI tools.”
Quote in the paperSubstantial claim - Findingown data
The total effect of intelligent TPACK on GenAI acceptance is β = 0.33.
“The total effect (total β = 0.33) also highlighted the overall impact when both direct and indirect paths were considered.”
Quote in the paperSubstantial claim - Findingown data
Perceived usefulness has no direct effect on teachers' acceptance of GenAI (β = 0.16, t = 1.35).
“However, perceived usefulness (H1: β = 0.16, t = 1.35) and intelligent TPACK (H6: β = 0.04, t = 0.40) showed no direct effect on teachers' acceptance of GenAI.”
- Also stated in: § 7.1. TAM Relationships · hedged
“The result for H1 showed a direct path coefficient (β = 0.16) with a non-significant tvalue (1.35), indicating that perceived usefulness did not significantly affect GenAI acceptance in this study.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Also stated in: § 7.1. TAM Relationships · hedged
- Findingown data
Perceived trust has a positive direct effect on perceived usefulness (β = 0.39).
“Similarly, H12 was strongly supported (β = 0.39), indicating that greater trust in GenAI enhances its perceived usefulness.”
Quote in the paperSubstantial claim - Findingown data
Perceived trust directly affects perceived usefulness (β = 0.39, t = 3.38).
“In addition, the results showed that perceived usefulness was directly affected by both perceived ease of use (H3: β = 0.28, t = 2.23*) and perceived trust (H12: β = 0.39, t = 3.38**), supporting H3 and H12.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
Intelligent TPACK has no statistically significant direct effect on perceived usefulness (β = 0.23).
“H4 showed a non-significant result (β = 0.23), indicating that the direct impact of intelligent TPACK on perceived usefulness was not statistically significant.”
- Also stated in: § 8. Conclusions, Limitations, and Future Work · plain
“However, the anticipated direct impacts of intelligent TPACK on the perceived usefulness and acceptance of GenAI were not supported.”
Quote in the paperSubstantial claim - Also stated in: § 8. Conclusions, Limitations, and Future Work · plain
- Findingown data
The total effect of AI literacy on perceived usefulness was significant (β = 0.25, t = 2.01).
“The total effect of AI literacy on perceived usefulness was significant (H7: β = 0.25, t = 2.01*).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
AI literacy has a significant indirect effect on perceived ease of use through intelligent TPACK (indirect β = 0.31).
“However, the significant indirect path through intelligent TPACK (indirect β = 0.31) implies that AI literacy helped improve the perceived ease of use by enhancing intelligent TPACK.”
Quote in the paperSubstantial claim - Findingown data
AI literacy has no direct effect on perceived ease of use (β = 0.08, t = 0.49).
“However, AI literacy had no direct effect on either perceived usefulness (H7: β = -0.13,t = 0.91) or perceived ease of use (H8: β = 0.08, t = 0.49), which resulted in the rejection of H7 and H8.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
The total effect of intelligent TPACK on GenAI acceptance was significant (β = 0.33, t = 3.20).
“The total effect of intelligent TPACK on GenAI was significant (H6: β = 0.33, t = 3.20**).”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Findingown data
AI literacy has no statistically significant direct effect on perceived ease of use (β = 0.08).
“The results for H8 also presented an interesting dynamic.The direct effect of AI literacy on perceived ease of use was not significant (β = 0.08), suggesting that more knowledge about GenAI does not straightforwardly make GenAI tools easier to use.”
Quote in the paperSubstantial claim - Findingown data
Perceived usefulness did not have a direct impact on GenAI acceptance.
“Contrary to expectations, perceived usefulness did not have a direct impact on acceptance, indicating potential gaps in aligning GenAI's capabilities with educators' needs.”
Quote in the paperSubstantial claim - Findingown data
AI literacy has no statistically significant direct effect on perceived usefulness (β = -0.13).
“The results for H7 presented an interesting dynamic, where the direct relationship between AI literacy and perceived usefulness was not significant (β = -0.13),suggesting that on its own, AI literacy does not directly lead to teachers viewing GenAI as more useful.”
Quote in the paperSubstantial claim - Findingown data
Intelligent TPACK has no statistically significant direct effect on GenAI acceptance (β = 0.04).
“The results for H6 showed that the direct influence of intelligent TPACK on GenAI acceptance was not significant (β = 0.04), suggesting that merely possessing intelligent TPACK does not directly lead to higher acceptance rates of GenAI technologies.”
- Also stated in: § 8. Conclusions, Limitations, and Future Work · plain
“However, the anticipated direct impacts of intelligent TPACK on the perceived usefulness and acceptance of GenAI were not supported.”
Quote in the paperSubstantial claim - Also stated in: § 8. Conclusions, Limitations, and Future Work · plain
- Premisecitation
Studies have shown that robust TPACK facilitates the adoption and integration of new technologies into teaching practices.
“Studies have shown that robust TPACK facilitates the adoption and integration of new technologies into teaching practices [38].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [38] Scherer, 2019, The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education, Comput. Educ. source
Not checked in the free run.
- Premisecitation
A recent systematic review by Rosli et al. highlighted emerging gaps in TAM, particularly in understanding how TAM constructs interact with newer technologies such as GenAI.
“A recent systematic review by Rosli et al. [30] on the advancements in and adaptations of TAM in the context of higher education highlighted that despite the robust framework of TAM, there are emerging gaps, particularly in understanding how these constructs interact with newer technologies, such as the GenAI in this study.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] Rosli, M.S., Saleh, N.S., Ali, A.M., Abu Bakar, S., and Tahir, L.M. (2022). A systematic review of the technology acceptance model for the sustainability of higher education during the COVID-19 pandemic and identified research gaps. Sustainability, 14. source
Not checked in the free run.
- Premisecitation
Specific drivers shaping teachers' intentions to adopt GenAI remain underexplored.
“While studies have investigated various dimensions of technological integration in classrooms, the specific drivers shaping teachers' intentions to adopt GenAI remain underexplored, necessitating the present research.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] Crompton, 2023, Artificial intelligence in higher education: The state of the field, Int. J. Educ. Technol. High. Educ. source
Not checked in the free run.
- Premisecitation
Perceived 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].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Nazaretsky, 2022, Teachers’ trust in AI-powered educational technology and a professional development program to improve it, Br. J. Educ. Technol. source
Not checked in the free run.
- Premisecitation
Studies have shown that educators who are more literate in AI find it easier to adopt and interact with AI technologies because they are better able to understand and navigate the functionalities of these systems.
“Studies have shown that educators who are more literate in AI find it easier to adopt and interact with AI technologies because they are better able to understand and navigate the functionalities of these systems [53].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [53] Chatterjee, 2020, Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling, Educ. Inf. Technol. source
Not checked in the free run.
- Premisecitation
AI literacy empowers teachers to understand and interact confidently with AI systems, addressing the skills gap that might hinder effective utilization.
“AI literacy is crucial because it empowers teachers to understand and interact confidently with AI systems, thus addressing the skills gap that might hinder effective utilization [31].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] Ng, 2021, Conceptualizing AI literacy: An exploratory review, Comput. Educ. Artif. Intell. source
Not checked in the free run.
- Premisecitation
Educators with higher AI literacy are more likely to appreciate the potential enhancements that AI can bring to educational environments, thus fostering a more receptive attitude towards accepting these technologies.
“Educators with higher AI literacy are more likely to appreciate the potential enhancements that AI can bring to educational environments, thus fostering a more receptive attitude towards accepting these technologies [48].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [48] Kong, 2022, Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building, Comput. Hum. Behav. Rep. source
Not checked in the free run.
- Premisecitation
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].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5] Nazaretsky, 2022, Teachers’ trust in AI-powered educational technology and a professional development program to improve it, Br. J. Educ. Technol. source
Not checked in the free run.
- cites [56] Amoozadeh, M., Daniels, D., Nam, D., Chen, S., Hilton, M., Ragavan, S.S., and Alipour, M.A. (2024, January 7). Trust in generative AI among students: An exploratory study. Proceedings of the 55th ACM Technical Symposium on Computer Science Education, Portland, OR, USA. source
Not checked in the free run.
- Interpretationown data
If GenAI is perceived as easy to use, teachers are more likely to adopt it.
“This means that if GenAI is perceived as easy to use, teachers are more likely to adopt it.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Premisecitation
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].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5] Nazaretsky, 2022, Teachers’ trust in AI-powered educational technology and a professional development program to improve it, Br. J. Educ. Technol. source
Not checked in the free run.
- cites [56] Amoozadeh, M., Daniels, D., Nam, D., Chen, S., Hilton, M., Ragavan, S.S., and Alipour, M.A. (2024, January 7). Trust in generative AI among students: An exploratory study. Proceedings of the 55th ACM Technical Symposium on Computer Science Education, Portland, OR, USA. source
Not checked in the free run.
- Interpretationown data
The relationship between AI literacy and perceived usefulness is entirely mediated by perceived trust.
“This suggests that the relationship between AI literacy and perceived usefulness is entirely mediated by perceived trust.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Interpretationown data
AI literacy indirectly influences perceived usefulness by enhancing trust in GenAI technologies.
“This indicates that AI literacy indirectly influences perceived usefulness by enhancing trust in GenAI technologies.”
Quote in the paperSubstantial claim - Interpretationown data
Perceived ease of use fully mediates the relationship between intelligent TPACK and GenAI acceptance.
“This means that perceived ease of use fully mediates the relationship between intelligent TPACK and GenAI acceptance.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Study design and conductown data
Discriminant validity was established through the heterotrait-monotrait (HTMT) ratio, with HTMT values consistently below 0.85.
“The square roots of the AVE values for all constructs (illustrated in Table 3) exceeded their respective intercorrelations, with HTMT values consistently below 0.85.”
Quote in the paperSubstantial claim - Study design and conductnone given
The cross-sectional nature of the study limited its ability to capture the dynamic process of technology adoption over time.
“Furthermore, the cross-sectional nature of the study limited its ability to capture the dynamic process of technology adoption over time.”
Quote in the paperNot a substantial claim - Interpretationown data
Intelligent TPACK can facilitate GenAI adoption by making the technology easier to use.
“However, the indirect path through the perceived ease of use was significant (indirect β = 0.23), indicating that intelligent TPACK can facilitate GenAI adoption by making the technology easier to use.”
Quote in the paperSubstantial claim - Interpretationown data
The link between AI literacy and perceived ease of use is fully mediated by intelligent TPACK.
“This implies that the link between AI literacy and perceived ease of use is fully mediated by intelligent TPACK.”
Quote in the paperSubstantial claim- Rests on an assumption: The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
- Rests on an assumption: The sample of 237 university teachers from one university is representative of university teachers in higher education.
- Study design and conductcitation
Convergent validity was affirmed through the average variance extracted (AVE) for each construct (AVE > 0.5).
“As recommended by Hair et al. [62], convergent validity was affirmed through the average variance extracted (AVE) for each construct (AVE > 0.5).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [62] Hair, 2019, When to use and how to report the results of PLS-SEM, Eur. Bus. Rev. source
Not checked in the free run.
- Premisecitation
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].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5] Nazaretsky, 2022, Teachers’ trust in AI-powered educational technology and a professional development program to improve it, Br. J. Educ. Technol. source
Not checked in the free run.
- cites [58] Gillath, 2021, Attachment and trust in artificial intelligence, Comput. Hum. Behav. source
Not checked in the free run.
- Study design and conductcitation
The sample size of 237 was adequate for SEM analysis, as Kline recommends a minimum of 200 participants for models with multiple constructs.
“The sample size of 237 was adequate for SEM analysis, as Kline [61] recommends a minimum of 200 participants, particularly for models with multiple constructs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [61] Kline, R.B. (2015). Principles and Practice of Structural Equation Modeling, The Guilford Press. [3rd ed.].
Not checked in the free run.
- May combine two claims. 9
- Classified as probably not a substantial claim. 21
May combine two claims.
FindingcitationThe Q2 value for GenAI acceptance was 0.38, demonstrating strong predictive relevance of the proposed model.
“In this study, the Q 2 value for GenAI acceptance was found to be 0.38.This demonstrates the strong predictive relevance of the proposed model.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationYang et al. highlighted the positive impact of teachers' TPACK on their perceived ease of use, usefulness, and overall acceptance of E-Schoolbag.
“Likewise, Yang et al. [40] highlighted the positive impact of teachers' TPACK on their perceived ease of use, usefulness, and overall acceptance of E-Schoolbag.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [40] Yang, 2021, A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag, Interact. Learn. Environ. source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationIntelligent TPACK ensures that university teachers are equipped to integrate AI-based tools within their specific pedagogical contexts, enhancing the relevance and effectiveness of GenAI in teaching and learning.
“Intelligent TPACK ensures that university teachers are equipped to integrate AI-based tools within their specific pedagogical contexts, enhancing the relevance and effectiveness of GenAI in teaching and learning [3].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] Celik, 2023, Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education, Comput. Hum. Behav. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTAM's two fundamental elements-perceived usefulness and perceived ease of use-greatly impact an individual's decision to embrace or discard a technology.
“According to Davis [27], the two fundamental elements of TAM-perceived usefulness and perceived ease of use-greatly impact an individual's decision to embrace or discard a technology.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Davis, 1989, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Q. source
Not checked in the free run.
May combine two claims.
PremisecitationTeachers who understand AI concepts and applications are more likely to recognize the benefits of GenAI, such as personalized learning and efficient student assessment, and therefore perceive these technologies as more useful.
“Teachers who understand AI concepts and applications are more likely to recognize the benefits of GenAI, such as personalized learning and efficient student assessment, and therefore perceive these technologies as more useful [45,51].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [45] Zhao, L., Wu, X., and Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modelling analysis. Sustainability, 14. source
Not checked in the free run.
- cites [51] Uygun, 2024, Teachers’ perspectives on artificial intelligence in education, Adv. Mob. Learn. Educ. Res. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSu and Yang observed that AI literacy programs enhance students' knowledge, skills, and attitudes towards AI.
“For instance, Su and Yang [49] observed that AI literacy programs enhance students' knowledge, skills, and attitudes towards AI, suggesting that such literacy contributes positively to students' perceptions of and competence using AI technologies.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [49] Su, J., and Yang, W. (2023). Artificial intelligence (AI) literacy in early childhood education: An intervention study in Hong Kong. Interact. Learn. Environ., 1–15. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationKaplan-Rakowski et al. indicated that perceived use had the strongest influence on teachers' perceptions of adopting GenAI in teaching and learning practices.
“Another study, by Kaplan-Rakowski et al. [9], indicated that perceived use had the strongest influence on teachers' perceptions of adopting GenAI in teaching and learning practices.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Grotewold, 2023, Generative AI and teachers’ perspectives on its implementation in education, J. Interact. Learn. Res.
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationHsu and Joo et al. found that perceived ease of use, perceived usefulness, and technology acceptance can be predicted by teachers' TPACK in educational technology adoption.
“Studies such as Hsu [39] and Joo et al. [33] have found that perceived ease of use, perceived usefulness, and technology acceptance can be predicted by teachers' TPACK in educational technology adoption.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [39] Hsu, 2016, Examining EFL teachers’ technological pedagogical content knowledge and the adoption of mobile-assisted language learning: A partial least square approach, Comput. Assist. Lang. Learn. source
Not checked in the free run.
- cites [33] Joo, 2018, Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model, J. Educ. Technol. Soc.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLee et al. investigated the impact of information literacy on user trust in government websites, revealing that higher levels of perceived information literacy improved perceptions of the websites' usefulness.
“Lee et al. [50] investigated the impact of information literacy on user trust in government websites, revealing that higher levels of perceived information literacy improved perceptions of the websites' usefulness.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] Lee, 2020, The effects of information literacy on trust in government websites: Evidence from an online experiment, Int. J. Inf. Manag. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationAccording to Wang et al., the degree of AI literacy can determine individuals' readiness to embrace AI technologies.
“According to Wang et al. [47], the degree of AI literacy can determine individuals' readiness to embrace AI technologies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [47] Wang, 2023, Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale, Behav. Inf. Technol. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMany studies have stressed the crucial role teachers play in introducing GenAI tools into classroom practice.
“many studies have stressed the crucial role teachers play in introducing GenAI tools into classroom practice [7,8].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [7] Bozkurt, 2023, Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift, Asian J. Distance Educ.
Not checked in the free run.
- cites [8] Mujiono, 2023, Educational collaboration: Teachers and artificial intelligence, J. Kependidikan J. Has. Penelit. Dan Kaji. Kepustakaan Di Bid. Pendidik. Pengajaran Dan Pembelajaran source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningAccording to TAM, perceived ease of use positively influences perceived usefulness.
“According to TAM, the relationship between these two concepts is such that perceived ease of use positively influences perceived usefulness.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMalik et al. highlighted the significance of perceived ease of use in influencing students' intentions to integrate AI chatbots into their teaching methodologies.
“Similarly, Malik et al. [29] highlighted the significance of perceived ease of use in influencing students' intentions to integrate AI chatbots into their teaching methodologies.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Malik, 2021, Adoption of chatbots for learning among university students: Role of perceived convenience and enhanced performance, Int. J. Emerg. Technol. Learn. source
Not checked in the free run.
May combine two claims.
PremisecitationChen et al. and Liao et al. used TAM to assess educators' acceptance of AI-driven educational tools, emphasizing the role of perceived usefulness in shaping positive attitudes and perceptions.
“Chen et al. [11] and Liao et al. [28] used TAM to assess educators' acceptance of AI-driven educational tools, emphasizing the role of perceived usefulness in shaping positive attitudes and perceptions.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [11] Chen, 2020, Application and theory gaps during the rise of artificial intelligence in education, Comput. Educ. Artif. Intell. source
Not checked in the free run.
- cites [28] Liao, Y.K., Wu, W.Y., Le, T.Q., and Phung, T.T.T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: The moderating role of e-WOM. Sustainability, 14. source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationcitationAI literacy is a key component in equipping teachers with the necessary skills and knowledge to integrate GenAI effectively into their teaching.
“This suggests that AI literacy is a key component in equipping teachers with the necessary skills and knowledge to integrate GenAI effectively into their teaching [45,48].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [45] Zhao, L., Wu, X., and Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modelling analysis. Sustainability, 14. source
Not checked in the free run.
- cites [48] Kong, 2022, Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building, Comput. Hum. Behav. Rep. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationTrust acts as a validation mechanism.
“Trust acts as a validation mechanism [58].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [58] Gillath, 2021, Attachment and trust in artificial intelligence, Comput. Hum. Behav. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLee et al. indicated that individuals' information literacy has a significant influence on their perceived trust in using government websites.
“Lee et al. [50] indicated that individuals' information literacy has a significant influence on their perceived trust in using government websites.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] Lee, 2020, The effects of information literacy on trust in government websites: Evidence from an online experiment, Int. J. Inf. Manag. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn the context of GenAI, complexity plays a unique role.
“In the context of GenAI, complexity plays a unique role [1].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [1] Lodge, 2023, Mapping out a research agenda for generative artificial intelligence in tertiary education, Australas. J. Educ. Technol. source
Not checked in the free run.
May combine two claims.
Study design and conductreasoningThe study's reliance on self-reported measures may have introduced bias and does not capture the real-time use or integration of GenAI in educational practices.
“In addition, the reliance on self-reported measures may have introduced bias, and such measures do not capture the real-time use or integration of GenAI in educational practices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe study received ethics approval from the Research Ethics Committee at King Faisal University (KFU-REC-2022-OCT-ETHICS258, issued on 25 October 2022).
“Research Ethics Committee at King Faisal University, KFU-REC-2022-OCT-ETHICS258, issued on 25 October 2022.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductcitationIndicator loadings for all constructs were between 0.72 and 0.94, indicating satisfactory to high levels of loading.
“All values are between 0.72 and 0.94, suggesting satisfactory to high levels of loading, as recommended by Hair et al. [62].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [62] Hair, 2019, When to use and how to report the results of PLS-SEM, Eur. Bus. Rev. source
Not checked in the free run.
May combine two claims.
InterpretationcitationAI literacy improves understanding of GenAI's advantages and functions, which encourages a favorable view of its acceptance and use in educational contexts.
“AI literacy improves understanding of GenAI's advantages and functions, which encourages a favorable view of its acceptance and use in educational contexts [3,47,48,64].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [3] Celik, 2023, Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education, Comput. Hum. Behav. source
Not checked in the free run.
- cites [47] Wang, 2023, Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale, Behav. Inf. Technol. source
Not checked in the free run.
- cites [48] Kong, 2022, Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building, Comput. Hum. Behav. Rep. source
Not checked in the free run.
- cites [64] Al-Abdullatif, A.M., and Alsubaie, M.A. (2024). ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy. Behav. Sci., 14. source
Not checked in the free run.
May combine two claims.
Study design and conductown dataInternal consistency was confirmed through Cronbach's alpha and composite reliability, with values of 0.81 ≥ α ≤ 0.94 and 0.87 ≥ CR ≤ 0.95 indicating good to high reliability.
“Next, the internal consistency was confirmed through Cronbach's alpha (α) and composite reliability (CR), with values of 0.81 ≥ α ≤ 0.94 and 0.87 ≥ CR ≤ 0.95 indicating good to high reliability.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenInformed consent was obtained from all subjects involved in the study.
“Informed consent was obtained from all subjects involved in the study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningCultural and regional differences were not within the scope of this research.
“As a result, cultural and regional differences, which may play a significant role in GenAI adoption, were not within the scope of this research.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe research primarily focused on individual-level factors influencing the acceptance of GenAI.
“It is important to acknowledge that the research primarily focused on individual-level factors influencing the acceptance of GenAI.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNazzal et al. confirmed that digital literacy is a strong predictor of user trust in the e-commerce context.
“Similarly, a study by Nazzal et al. [60] confirmed that digital literacy is a strong predictor of user trust in the e-commerce context.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [60] Nazzal, 2022, The effect of digital literacy and website quality on purchase intention in internet shopping through mediating variable: The case of internet users in Palestine, Webology source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningGenAI tools often incorporate advanced functionalities like natural language processing, content generation, and adaptive learning systems, potentially overwhelming or technically challenging for educators unfamiliar with these technologies.
“Unlike more conventional educational technologies, GenAI tools often incorporate advanced functionalities like natural language processing, content generation, and adaptive learning systems, potentially overwhelming or technically challenging for educators unfamiliar with these technologies.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The cross-sectional survey data and SEM path coefficients can support causal/directional conclusions about the relationships among the constructs.
“The study employed structural equation modeling (SEM) to determine the relationships among the constructs.”
“Perceived ease of use (H2: β = 0.48, t = 4.53**) and AI literacy (H10: β = 0.35, t = 3.83**) were found to be the two factors directly affecting GenAI's acceptance”
The paper relies on this without support.
The sample of 237 university teachers from one university is representative of university teachers in higher education.
“Data were gathered from a sample of 237 university teachers through a structured questionnaire.”
“We invited approximately 600 university teachers and received 237 completed responses, yielding a response rate of 40%.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-TPACK is more likely to relate to innovative practice when teachers possess sufficient AIL and strong TSE.
“In other words, AI-TPACK is more likely to relate to innovative practice when teachers possess sufficient AIL and strong TSE (Al-Abdullatif, 2024; Joo et al., 2018).”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Al-Abdullatif, 2024
Section 7.3 (page unavailable) reports a strong relationship between AI literacy and intelligent TPACK and discusses their role in integrating GenAI into teaching. But the paper does not establish that AI-TPACK relates more strongly to innovative practice when teachers have sufficient AI literacy and strong teacher self-efficacy (TSE); its reported outcomes concern GenAI acceptance, and it does not examine TSE as a condition. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrior studies grounded in the TPACK framework report significant associations between teachers' technology integration knowledge and the development of AIL.
“Prior studies grounded in the TPACK framework report significant associations between teachers’ technology integration knowledge and the development of AIL (Al-Abdullatif, 2024; Pehlevan and Ünal, 2024; Wilton et al., 2024).”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Al-Abdullatif, 2024
Section 7.3, “AI Literacy Relationships” (page unavailable), reports a significant relationship between AI literacy and intelligent TPACK (β = 0.64). But it describes AI literacy as enhancing teachers’ intelligent TPACK, not technology-integration knowledge as contributing to the development of AI literacy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredA significant tension emerged in the literature regarding whether technical proficiency and practical experience should precede ethical training or whether ethical foundation should come first.
“A significant tension emerged in the literature regarding the priority of training.”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Al-Abdullatif, 2024
Sections 7.2 and 7.4 recommend hands-on GenAI experience and attention to ethical concerns, respectively. Neither discusses a tension over which training should come first; searches for training priority and sequencing found no such passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Results reverse the direction of the AI-literacy paths
MajorThe results say intelligent TPACK and perceived trust influenced AI literacy. The stated H9 and H11 paths, which the paper later discusses as supported, run in the opposite direction.
“Furthermore, the results indicated that AI literacy was directly influenced by both intelligent TPACK (H9: β = 0.64, t = 8.55**) and perceived trust (H11: β = 0.30, t = 2.44*).”
“AI literacy emerged as a crucial factor, directly affecting acceptance, intelligent TPACK, and perceived trust in GenAI.”
R2 and Q2 values are attributed to the wrong table
MinorThe text directs readers to Table 3 for R2 and Q2 values, but Table 3 is the correlation matrix; those values appear in Table 2.
“Table 3 provides the detailed R 2 and Q 2 values.”
“Table: 3 Table 3 . Correlation matrix and discriminant validity analysis.”
HTMT validity threshold is stated in opposite directions
MinorThe measurement-model text says HTMT should be greater than 0.85 to indicate discriminant validity, then cites values below 0.85 as evidence that validity was established.
“Ideally, this ratio should be greater than 0.85 to indicate discriminant validity.”
“The square roots of the AVE values for all constructs (illustrated in Table 3) exceeded their respective intercorrelations, with HTMT values consistently below 0.85.”
Mediated AI-literacy effect is called a direct effect
MinorThe results first report no significant direct effect of AI literacy on perceived usefulness, then call the effect facilitated by perceived trust a direct impact. The latter is an indirect, mediated effect.
“However, AI literacy had no direct effect on either perceived usefulness (H7: β = -0.13,t = 0.91) or perceived ease of use (H8: β = 0.08, t = 0.49), which resulted in the rejection of H7 and H8.”
“However, AI literacy was found to have a direct impact on perceived usefulness, facilitated by the mediating role of perceived trust (H7: β = 0.12, t = 2.10*).”
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Analyzing Teacher Competency with TPACK for K-12 AI Education
Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S., & Kim, H. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00731-9 · Cited by 111 · see citing works
Su, J., & Yang, W. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2217864 · Cited by 110 · see citing works
Al-Abdullatif, A. M. (2023) · Education Sciences · doi:10.3390/educsci13111151 · Cited by 102 · see citing works
Self-citationTeachers’ perspectives on artificial intelligence in education
Uygun, D. (2024) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2024.01.005 · Cited by 99 · see citing works
Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i18.24315 · Cited by 93 · see citing works
Rane, N. (2024) · TESOL and Technology Studies · doi:10.48185/tts.v5i1.1000 · Cited by 86 · see citing works
A study of K-12 teachers’ TPACK on the technology acceptance of E-schoolbag
Yang, J., Wang, Q., Wang, J., Huang, M., & Ma, Y. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2019.1627560 · Cited by 83 · see citing works
Artificial intelligence-based text generators in hepatology: ChatGPT is just the beginning
Ge, J., & Lai, J. C. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000097 · Cited by 83 · see citing works
Al-Abdullatif, A. M., Al-Dokhny, A. A., & Drwish, A. M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1129070 · Cited by 80 · see citing works
Self-citationThohir, M., Ahdhianto, E., Mas'ula, S., Yanti, F. A., & Sukarelawan, M. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/12918 · Cited by 59 · see citing works
Rane, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4599104 · Cited by 53 · see citing works
Mailizar, M., Hidayat, M., & Almanthari, A. (2021) · Journal of Digital Learning in Teacher Education · doi:10.1080/21532974.2021.1934613 · Cited by 49 · see citing works
Effective or ineffective? Using ChatGPT for staffing in the hospitality industry
Elmohandes, N. M. A. A., & Marghany, M. (2023) · European Journal of Tourism Research · doi:10.54055/ejtr.v36i.3286 · Cited by 33 · see citing works
Drivers of Chatbot Adoption among K–12 Teachers in Saudi Arabia
Al-Amri, N. A., & Al-Abdullatif, A. M. (2024) · Education Sciences · doi:10.3390/educsci14091034 · Cited by 27 · see citing works
Self-citationNazzal, A., Thoyib, A., Zain, D., & Hussein, A. S. (2022) · Webology · doi:10.14704/web/v19i1/web19163 · Cited by 13 · see citing works
Educational Collaboration : Teachers and Artificial Intelligence
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Cited by
Sorted by citations
Huang, L., & Zhao, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1669247 · Cited by 55 · see citing works
“It plays an essential role in curriculum development, formative assessment, and personalized instruction (Al-Abdullatif, 2024; Ning et al., 2025).”
Đerić, E., Frank, D., & Milković, M. (2025) · Information · doi:10.3390/info16070622 · Cited by 52 · see citing works
Khoso, A. K., Honggang, W., & Darazi, M. A. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06362-x · Cited by 40 · see citing works
“AI-driven tools, from intelligent tutoring systems to conversational agents, provide immediate feedback and adaptive content (Özçelik, 2025), potentially revo-lutionizing language acquisition (Al-Abdullatif, 2024).”
Deroncele-Acosta, Á., Sayán-Rivera, R. M. E., Mendoza-López, A. D., & Norabuena-Figueroa, E. (2025) · Applied System Innovation · doi:10.3390/asi8030083 · Cited by 45 · see citing works
Exploring emergent AI-TPACK competencies in a two-week AI literacy module for preservice teachers
Aldemir, T., Bicer, A., Kilinc, S., Moon, J., & Kwok, M. (2025) · Teaching and Teacher Education · doi:10.1016/j.tate.2025.105231 · Cited by 27 · see citing works
“Developing AI literacy is crucial: higher AI knowledge is associated with greater readiness to adopt AI tools, fewer misconceptions, and a higher perceived usefulness (Al-Abdullatif, 2024; Wang et al., 2022).”
Pan, L., Luo, H., & Gu, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3546572 · Cited by 24 · see citing works
“Al-Abdullatif [48] investigated the acceptance of AI in higher education and found that AI-L can significantly influence university faculty’s BI but not PU and PEOU.”
Teachers’ artificial intelligence (AI) literacy: an exploratory study
Deshen, M., Harari, R., & Aharony, N. (2026) · Smart Learning Environments · doi:10.1186/s40561-026-00433-5 · Cited by 40 · see citing works
“Among educators, it plays a crucial role in adopting ChatGPT, shaping their willingness to use it (Al-Abdullatif, 2024; Bhat et al., 2024).”
Al-Abdullatif, A. M. (2025) · Systems · doi:10.3390/systems13060490 · Cited by 49 · see citing works
Self-citationBewersdorff, A., Nerdel, C., & Zhaı, X. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100493 · Cited by 30 · see citing works
“Third, surrounding factors such as institutional support structures and social influences are underexplored, despite indications that they play a critical role in the development and application of AI literacy (Al-Amri & Al-Abdullatif, 2024; Shen & Cui, 2024 ).”
Huang, Z., Fu, X., & Zhao, J. (2025) · Sustainability · doi:10.3390/su17125497 · Cited by 27 · see citing works
Iqbal, J., Asgarova, V., Hashmi, Z. F., Ngajie, B. N., Asghar, M. Z., & Järvenoja, H. (2025) · Discover Computing · doi:10.1007/s10791-025-09655-6 · Cited by 32 · see citing works
“It also incorporates teacher education models like TPACK (Mishra et al. 2023; Yang et al. 2025) and AI literacy frameworks (Al-Abdullatif 2024; Lan et al. 2025), which are vital for understanding teacher readiness, development, and ethics in AI-enhanced instruction.”
Li, Y., Castulo, N. J., & Xu, X. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1505856 · Cited by 32 · see citing works
Arslan, S. (2025) · System · doi:10.1016/j.system.2025.103680 · Cited by 28 · see citing works
Tan, X. J., Cheng, G., & Ling, M. H. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100521 · Cited by 39 · see citing works
Asghar, M. Z., Duah, K. A., Iqbal, J., & Järvenoja, H. (2025) · Discover Computing · doi:10.1007/s10791-025-09691-2 · Cited by 20 · see citing works
Liu, C., Yang, L., Dong, X., & Li, X. (2025) · Systems · doi:10.3390/systems13080639 · Cited by 16 · see citing works
Pachava, V., Lasekan, O. A., Alarcón, C. M. M., Pena, M. T. G., & Golla, S. K. (2025) · Journal of Lifestyle and SDGs Review · doi:10.47172/2965-730x.sdgsreview.v5.n02.pe03774 · Cited by 22 · see citing works
“The framework redefines stakeholder roles, transitioning faculty into mentors, empowering students with adaptive pathways, and enabling administrators to optimize workflows through predictive analytics and strategic planning (Al-Abdullatif, 2024).”
InfluentialPhysics instructors’ acceptance and implementation of generative AI
Wattanakasiwich, P., Kaewkhong, K., & Katwibun, D. (2025) · Physical Review Physics Education Research · doi:10.1103/r2fn-kdy4 · Cited by 19 · see citing works
Mellado, R., & Cubillos, C. (2025) · Applied Sciences · doi:10.3390/app15115867 · Cited by 18 · see citing works
Empowered by AI: exploring the link between literacy, self-efficacy, and career expectations
Ho, N. T. T., & Le, H. V. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106408 · Cited by 13 · see citing works
Eyal, L. (2025) · Education Sciences · doi:10.3390/educsci15111452 · Cited by 18 · see citing works
“Al-Abdullatif (2024) developed the Intelligent TPACK framework and validated it through structural equation modeling with 312 faculty members.”
Syed, A. Z., Memon, Z. H., Khan, K., Hameed, I., & Nadeem, M. (2025) · On the Horizon The International Journal of Learning Futures · doi:10.1108/oth-02-2025-0019 · Cited by 12 · see citing works
Tan, Q. (2025) · British Educational Research Journal · doi:10.1002/berj.70059 · Cited by 16 · see citing works
Yang, P., Liu, Y., & Li, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-21795-9 · Cited by 17 · see citing works
“Al-Abdullatif 25 involved 237 university teachers to evaluate factors influencing their acceptance of generative AI (GenAI) technologies.”
Basil, M., Ahmed, W., Hajeomar, R., Strawbridge, J., Lynch, M. R., & Mukhalalati, B. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08527-3 · Cited by 13 · see citing works
A Critical Review of Privacy, Bias, and Trust Challenges in Generative AI for Educational Contexts
Kumar, G., & Vavekanand, R. (2025) · IEEE International Conference on Data and Software Engineering (ICoDSE) · doi:10.1109/icodse68111.2025.11351807 · Cited by 9 · see citing works
“Audits and enhancements to meet fairness in any AI model are increasingly demanded by stakeholders to eliminate the possibility of continuing any in-equities [8].”
InfluentialБаггиа, А., Tratnik, A., & Brezavšček, A. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02717-x · Cited by 24 · see citing works
Generative AI in Chinese Early Childhood Education
Sun, M., Yan, R., & Wen, R. (2025) · International Journal of Teacher Education and Professional Development · doi:10.4018/ijtepd.382379 · Cited by 11 · see citing works
Akanda, F., & Talukder, T. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102437 · Cited by 15 · see citing works
Multidimensional determinants of generative AI acceptance in foreign language education
Xu, T., & Xiong, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-36700-1 · Cited by 6 · see citing works
“In line with previous studies on AI-based technology for EU citizens 66 and GenAI educational resources for university instructors 121 , the findings empirically support the hypothesis that people who are more effectively able to recognize, identify, and evaluate GenAI-related products are more…”
Onbaşılı, Ü. İ. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13991-5 · Cited by 8 · see citing works
Oo, C. T. L., Wider, W., Pang, N. T. P., Koh, E. B. Y., Vasanthi, R. K., Thet, K. Z. Z., Ramalho, R., Özdemir, B. N., & Mahboob, K. (2026) · International Journal of Qualitative Studies on Health and Well-being · doi:10.1080/17482631.2026.2621802 · Cited by 5 · see citing works
“Studies have shown that usability and accessibility significantly influence user trust and intention to use a system (Al-Abdullatif, 2024).”
Ober, J., Матусевич, Т. В., & Strutynska, O. (2025) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2025.100639 · Cited by 5 · see citing works
Milutinović, V. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13786-0 · Cited by 5 · see citing works
“Educators proficient in AI_LIT are more confident in navigating ethical dilemmas, applying AI responsibly, and integrating AI within the TPACK framework (Al-Abdullatif, 2024).”
Artificial Intelligence and Formative and Shared Assessment in Teacher Education
Fuentes-Nieto, T., Herguedas, J. L. A., & Pastor, V. M. L. (2025) · Applied Sciences · doi:10.3390/app152212067 · Cited by 5 · see citing works
Joshi, S., Pandey, K., & Maheta, D. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2597051 · Cited by 7 · see citing works
“Empirical evidence indicates that an increase in trust positively influences behavioral intention in different education settings (Al-Abdullatif, 2024; Shin, 2021).”
Teacher readiness for generative AI: A Theory of Planned Behaviour approach
Chapman, B. L., Ross, A. S., & Petraki, E. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102351 · Cited by 12 · see citing works
Jaiswal, R., Prakhar, P., Chen, P.-J., & Gupta, S. (2026) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2025.2602583 · Cited by 5 · see citing works
“Access to digital tools, structured professional development, and a collaborative institutional culture have improved educators’ confidence and effectiveness in using AI (Al-Abdullatif, 2024; Tram, 2024).”
InfluentialCunningham, E., Kings, I., Robbins, P. T., & Keith, M. J. (2026) · Education for Chemical Engineers · doi:10.1016/j.ece.2026.100503 · Cited by 5 · see citing works
Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts
Giansanti, D., & Cosenza, C. (2026) · AI · doi:10.3390/ai7040121 · Cited by 9 · see citing works
Factors of AI Adoption in Educational Contexts: A Meta-Analytic Synthesis Through the UTAUT
Zhuang, Y., Li, R., & Duan, S. (2025) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel65868.2025.11193611 · Cited by 5 · see citing works
“The effectiveness of technology in education is predicated on the willingness of educators to embrace it and integrate it into their teaching strategies, as well as the willingness of students to use it as learning occurs [4].”
Generative AI in Research Group Formation: Academic Perceptions and Institutional Pathways
Sawair, F., & Ghazzawi, H. (2025) · Information · doi:10.3390/info16121081 · Cited by 4 · see citing works
Reconceptualizing of GenAI adoption in higher education: A task-based perspective
Chaieb, M., Cuel, R., & Bouzaabia, R. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101365 · Cited by 6 · see citing works
Šerić, M., & Praničević, D. G. (2026) · Education and Information Technologies · doi:10.1007/s10639-025-13889-8 · Cited by 6 · see citing works
Exploring the scope of artificial intelligence literacy in education: A bibliometric approach
Shoustikova, T. V., Zaripova, Z. F., Ибрагимов, Г. И., Zaitseva, N. A., Pashanova, O. V., & Lobuteva, A. V. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/17892 · Cited by 5 · see citing works
Faculty AI literacy in higher education systems: a systematic review and future-adaptive framework
Wu, Y., & Huang, Y. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2648739 · Cited by 4 · see citing works
“…et al., 2025), preparing students for AI-driven workplaces (Lérias et al., 2024; J. Zhang & Zhang, 2024), developing students’ ability to collaborate with AI systems (Al-Abdullatif, 2024; Xue, 2025), and fostering students’ innovative thinking and adaptability (Dilek et al., 2025; Hoang, 2025).”
InfluentialYang, J., Öge, K., von Mühlenen, A., Akbulut, A. B., Carey, T. S., & Okorro, C. (2026) · Education Sciences · doi:10.3390/educsci16060838 · Cited by 4 · see citing works
“These constructs remain highly relevant in GenAI contexts, particularly in explaining variations in literacy, confidence, and perceptions of effectiveness (Al-Abdullatif, 2024).”
Three inseparable facets and five new knowledge domains: An extended GenAI-TPACK proposal
Demir, S., Uşak, M., & Кодиров, О. С. (2026) · Pedagogical Research · doi:10.29333/pr/18714 · Cited by 4 · see citing works
“Al-Abdullatif (2024) describes AI literacy as the capability of an individual to utilize AI applications, to evaluate the capacity and limitations of these apps, to respect ethical standards and to be conscious about privacy and security issues.”
Kaya, D., Ursavaş, Ö. F., & Durak, H. Y. (2025) · European Journal of Education · doi:10.1111/ejed.70216 · Cited by 4 · see citing works
Mesterséges intelligencia a felsőoktatásban
Nagy, J. T., Rajki, Z., & Dringó-Horváth, I. (2025) · Iskolakultúra · doi:10.14232/iskkult.2025.7.3 · Cited by 5 · see citing works
Shahzad, M. F., Xu, S., & Zahid, H. (2025) · SHS Web of Conferences · doi:10.1051/shsconf/202522301001 · Cited by 3 · see citing works
One size does not fit all: customizing teaching and learning strategies with Generative AI
Núñez, N. A., Fernández-Concha, R., & Cornejo-Meza, G. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1699228 · Cited by 3 · see citing works
Wang, Z., Wang, K., Park, S., & Yan, J. (2026) · Current Psychology · doi:10.1007/s12144-025-08966-7 · Cited by 5 · see citing works
“…settings, it is also common for users to reject AI recommendations due to insufficient trust, underscoring the need to consider emotional factors in the trust-building process between AI literacy and perceived capabilities (Al Abdullatif, 2024; Hajianvari & Ramazani, 2024; Longoni et al., 2019).”
Zhu, Z., Gan, Q., & Duan, P. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-06692-4 · Cited by 6 · see citing works
Huiqing, Z., & Junjie, P. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106480 · Cited by 5 · see citing works
Che, C. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1755238 · Cited by 10 · see citing works
Teacher Leadership and AI Literacy
Çoban, Ö., & Durnalı, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0472-4.ch001 · Cited by 3 · see citing works
Kılıç, E., & Çelik, H. E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100452 · Cited by 9 · see citing works
A DEMATEL-ANP-Based Evaluation of AI-Assisted Learning in Higher Education
Ilieva, G., Yankova, T., Ruseva, M., & Klisarova-Belcheva, S. (2026) · Computers · doi:10.3390/computers15020079 · Cited by 3 · see citing works
“Competency-oriented frameworks such as Intelligent-TPACK [32,33] focus on educators’ knowledge and readiness but do not offer comprehensive models for assessing student outcomes or institutional oversight.”
Williams, A. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2698137 · Cited by 2 · see citing works
Assessing artificial intelligence literacy in foreign language teachers: a TPACK-based perspective
Ren, T., & Li, Q. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1706559 · Cited by 5 · see citing works
“This item development framework is consistent with current empirical models advocating for the integration of AI-TPACK in language teacher education (Al-Abdullatif, 2024; Xue, 2024; Vo and Huynh, 2025).”
Engagement in LLM chatbot-supported learning: The pivotal roles of GenAI competency and emotion
Huang, X., & Zhang, S. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100559 · Cited by 2 · see citing works
Li, P., Islam, M., Zaini, K., & Ravindran, L. (2026) · Asia Pacific Journal of Education · doi:10.1080/02188791.2026.2638801 · Cited by 2 · see citing works
Teachers’ adoption of generative artificial intelligence (GenAI) in educational settings
Deshen, M., Harari, R., & Aharony, N. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2624877 · Cited by 4 · see citing works
Xu, J., Tian, M., Peng, F., Hu, D., & Liu, M. (2026) · BMC Psychology · doi:10.1186/s40359-026-04484-8 · Cited by 6 · see citing works
“In turn, higher levels of AI literacy and AI self-efficacy may be related to teachers’ continued exploration of more creative and adaptive strategies for applying GAI in teaching [17, 57].”
Alshaie, F. S., Alshdokhi, K. A., & Alrefaee, Y. M. H. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03172-2 · Cited by 2 · see citing works
“For AI adoption specifically, AI literacy and perceived ease prove most influential while intelligent TPACK and trust mediate relationships [5].”
Primary school teachers’ I-TPACK competencies after gemini-based professional development
Hussein, A., Daher, W. M., & Assali, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1800871 · Cited by 2 · see citing works
Asag, M. A., & Mamun, M. A. A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100614 · Cited by 4 · see citing works
Younas, M., & Noor, U. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2707309 · Cited by 2 · see citing works
Torralba, E. M. (2025) · Schrödinger Journal of Physics Education · doi:10.37251/sjpe.v6i3.2031 · Cited by 2 · see citing works
“This aligns with the Technology Acceptance Model, which suggests that perceived usefulness and ease of use drive technology adoption [49].”
Huynh-Thanh, T., Dinh, T. H. N., Teow, J., Stibe, A., & Nguyen, K. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02060-4_14 · Cited by 1 · see citing works
Bekešienė, S., Smaliukienė, R., & Vasiliauskas, A. V. (2025) · Sustainability · doi:10.3390/su17209151 · Cited by 2 · see citing works
Yıldız, T. A., Yağcı, Ş. Ç., & Orhan, A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2583196 · Cited by 2 · see citing works
“These results are supported by previous research showing that AI literacy directly affects adoption intentions and also indirectly influences them through perceived usefulness and perceived ease of use (Al-Abdullatif, 2024).”
Pedagogically Intelligent Smart Campuses for AI-Mediated and Equity-Centered Knowledge Production
Alam, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6831-3.ch010 · Cited by 1 · see citing works
Anwer, B., Brohi, R., & Alvi, A. (2025) · Futurity Education · doi:10.57125/fed.2025.12.01 · Cited by 1 · see citing works
“These findings align with those of Al-Abdullatif (2024) and Almahasees & Qutech, who highlighted that structured assignments and instructor endorsement enhance students’ confidence and acceptance of AI-assisted learning, particularly in resource-constrained higher education contexts.”
Redefining Teacher Professionalism in the Age of Artificial Intelligence: An Indonesian Perspective
Kusumaningrum, S. R., & Rosydiana, E. A. (2026) · KnE Social Sciences · doi:10.18502/kss.v11i2.20731 · Cited by 2 · see citing works
Habur, A. M., & Gaol, E. L. (2026) · International Studies in Catholic Education · doi:10.1080/19422539.2026.2631456 · Cited by 1 · see citing works
Ma, T., & Zhang, W. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1755301 · Cited by 3 · see citing works
Birhanu, E. T., & Jahara, S. F. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2677729 · Cited by 1 · see citing works
Mersin, N. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261456930 · Cited by 2 · see citing works
“…these tools can be integrated with instructional strategies, they are better able to evaluate their concrete contributions to supporting instructional processes, enhancing student engagement, and improving performance, and thus perceive them as more useful (Al Abdullatif, 2024; Runge et al., 2025).”
InfluentialBeták, N., & Szőköl, I. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1813449 · Cited by 1 · see citing works
Yang, Z., Zhang, L. J., & Zhang, Y. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2695231 · Cited by 1 · see citing works
Cao, S., Liu, X., & Yu, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1797047 · Cited by 1 · see citing works
“Al-Abdullatif (2024) stated that teachers with high AI literacy are better equipped to adapt to the educational changes brought about by AI technology and adopt new teaching methods in their own teaching.”
Qirui, C., & Osman, K. (2026) · Information · doi:10.3390/info17070688 · Cited by 1 · see citing works
“In this sense, AI integration in teacher education should be understood as a curriculum and pedagogy issue, not merely as tool adoption [8,9].”
Dahri, N. A., Hussain, A., Amjad, A. I., Fauadi, M. H. F. M., & Soomro, R. B. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108287 · Cited by 1 · see citing works
Lin, J., & Al‐Hada, N. M. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1906070 · Cited by 2 · see citing works
“…structure extends Shulman's (1986) pedagogical content knowledge construct into the GenAI era and aligns with emerging Intelligent-TPACK frameworks (Al-Abdullatif, 2024; Wang et al., 2024; Chen et al., 2025) that specify AI technological knowledge, AI technological pedagogical knowledge, and AI…”
Boor, M. C. H., MOHAMMED, S., & Musa, Y. (2026) · Journal of Studies in Science and Mathematics Education · doi:10.67203/jossme.2026.bwsd2sjn · Cited by 1 · see citing works
User Experience Analysis: ChatGPT-based AI in Islamic Religious Education Learning
Nisa, W., & Sulaiman, M. H. (2025) · Studies in Learning and Teaching · doi:10.46627/silet.v6i1.612 · Cited by 3 · see citing works
Bautista, A., & Carrao, L. J. (2025) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie67206.2025.11283183 · Cited by 1 · see citing works
“Al-Abdullatif [6] also found that teachers ’ perceived trust in the technology and their AI literacy significantly affect teachers' acceptance of GenAI.”
InfluentialHaidar, R., & Tassis, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7014667/v1 · Cited by 1 · see citing works
Bai, X., & Hsieh, T.-S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1834827
“Prior studies grounded in the TPACK framework report significant associations between teachers’ technology integration knowledge and the development of AIL (Al-Abdullatif, 2024; Pehlevan and Ünal, 2024; Wilton et al., 2024).”
Batubara, H. H. (2025) · At-Thullab Jurnal Pendidikan Guru Madrasah Ibtidaiyah · doi:10.30736/atl.v9i1.2405 · Cited by 1 · see citing works
Cano, C. A. G., Castillo, V. S., & Zapata, E. M. J. (2025) · REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA · doi:10.24054/rcta.v2i46.3743 · Cited by 2 · see citing works
Utilization of AI by the doctors for patient care in tertiary care hospital PeshawarPreprint
Faiz, A., Haq, R. U., Rehman, A. U., Ullah, H., Zahir, Q., & Qayyum, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7290827/v1
Reimagining Decent Work in AI-Driven Workplaces: The Role of Literacy, Trust and SupportPreprint
Thakur, S., & Tiwari, D. V. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5434807
Ethical AI Storytelling in Volunteer Tourism
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“…performance and effort expectancy, social influence, facilitating conditions, and hedonic motivations significantly predict intention to use generative AI among university faculty, [9] found that AI literacy shapes perceived ease of use, directly leading to higher acceptance of generative AI tools.”
InfluentialAlhiane, M., Meliani, H. E., & Nafidi, Y. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07785-1_24
The Strategic Role of School Principals in Realizing Student-Centered Learning
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Teachers' Perception and Trust in the Use of AI Chat: A Survey on Its Role in Education
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- Authors
- Al-Abdullatif, A. M.
- Affiliations
- King Faisal University (SA)
- Corresponding author
- Al-Abdullatif, Ahlam Mohammed
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2024-11-03
- Volume
- 14
- Issue
- 11
- Pages
- 1209
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4404046812
- Semantic Scholar
- ef816dd1894cba47045b71355b7ac0aabf99d994
- Cited by
- 169
- References
- 62
- Citations by year
- 2026: 115 · 2025: 53 · 2024: 1
- FWCI
- 63.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research, Vice Presidency of Graduate Studies and scientific research at King Faisal University (A374)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- generative AI, teacher acceptance, AI literacy, perceived trust, perceived ease, technology acceptance model, higher education teachers, teacher professional development, AI in teaching
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