Conference Agenda
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10 SES 16 B: Teaching Science
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10. Teacher Education Research
Paper Exploring In-Service Physics Teachers’ Understanding of Students’ Alternative Conceptions about Heat and Internal Energy 1: İstanbul Gelişim University, Turkey (Türkiye); 2: Boğaziçi University Presenting Author:Teaching requires more than transmitting disciplinary knowledge; it involves the integration of multiple forms of professional knowledge to support meaningful learning (Wilson, Shulman, & Richert, 1987). Shulman (1987) conceptualized this specialized knowledge as pedagogical content knowledge (PCK), defined as the blending of content and pedagogy that enables teachers to transform subject matter into forms understandable for students. PCK is widely recognized as a multidimensional and topic-specific construct, encompassing knowledge of student understanding, instructional strategies, curriculum, and assessment (Baumert & Kunter, 2013; Park & Chen, 2012). Among these dimensions, knowledge of student understanding is particularly central to coherent teaching practice. Contemporary learning theories emphasize that students interpret new ideas through their existing conceptions (Duit & Treagust, 2012), highlighting the importance of teachers’ awareness of students’ alternative conceptions. Research indicates that teachers’ PCK strongly influences instructional quality and student outcomes (Coe et al., 2014). Despite growing interest in PCK, studies focusing on physics teachers remain limited (Chan et al., 2019). Heat and temperature represent a topic in which students frequently hold persistent alternative conceptions (Osborne & Cosgrove, 1983; Jasien & Oberem, 2002). However, little is known about how in-service physics teachers interpret these conceptions, underscoring the need for further research. In this regard, this study explores in-service physics teachers’ understanding of pervasive student alternative conceptions about heat. In particular, the study focuses on two student alternative conceptions frequently stated in literature: (1) treating heat as a substance-like material which is possessed by objects, and (2) assuming that objects receiving the same amount of heat should have equal internal energy. The study employs a qualitative descriptive design and uses a vignette-based paper-and-pencil form for data collection. The study participants are 90 in-service physics teachers. Vignette-based items were used because they present teachers with authentic classroom situations. Specifically, the vignettes presented physics teachers with snapshots of classroom situations related to teaching heat and temperature, and asked them to identify underlying alternative conceptions or possible incorrect statements that students might produce. The results suggested that physics teachers had difficulty identifying students’ underlying conceptions about heat and internal energy presented in the vignettes. They frequently relied on overly broad generalizations, and in some cases, teachers’ responses suggested that they themselves held the alternative conception of heat as a substance-like entity possessed by objects. Specifically, most physics teachers tend to oversimplify the alternative conception of “substances possess heat” with a more broad student difficulty as “students confuse heat and temperature. Moreover, most of the physics teachers could not infer the alternative conception “objects receiving the same amount of heat should have equal internal energy” and made irrelevant interpretations regarding students’ understanding. Methodology, Methods, Research Instruments or Sources Used A qualitative descriptive design was used in this study. Ninety in-service physics teachers participated in the study. A vignette-based paper-and-pencil form was used as the main data collection tool. Descriptive statistics were employed to classify teachers’ responses as correct, partially correct, or false/irrelevant. In addition, expert opinions were obtained from two physics education professors regarding the content and clarity of the items. The items specifically targeted the following two alternative conceptions: (1) treating heat as a substance-like material that is possessed by objects, and (2) assuming that objects receiving the same amount of heat should have equal internal energy. Teachers’ responses to the written form were qualitatively analyzed and classified as correct, partially correct, or false/irrelevant using a classification scheme. The classification scheme consisted of exemplary teacher responses for each category and was developed based on analyses of the relevant literature. Interrater reliability was calculated to examine the consistency of the coding process. Conclusions, Expected Outcomes or Findings Findings of the study indicated that all physics teachers were aware that students hold problematic ideas about heat and temperature and commonly cited examples such as “heat and temperature are the same.” However, most teachers were unable to accurately identify the alternative conception that substances possess heat. When asked to interpret a student statement reflecting this idea, very few teachers explicitly wrote that heat is an energy transferred between objects and cannot be possessed by matter. The remaining teachers tended to focus on secondary aspects of student thinking, such as neglecting mass or attending only to temperature, suggesting that they did not recognize the underlying source of the error. In addition, one physics teacher implicitly demonstrated this alternative conception by claiming that an ocean has more heat than a matchstick, indicating that the teacher himself viewed heat as a property of objects. Teachers’ understanding of alternative conceptions about internal energy also appeared fragile. In one item, teachers were asked to select possible incorrect student responses and explain the reasoning behind them. Although 69% of teachers correctly identified plausible incorrect answers, only 36% were able to articulate the underlying alternative conception. Several teachers merely repeated students’ incorrect statements or offered vague explanations, such as insufficient understanding of heat and temperature, rather than identifying specific misconceptions. In sum, results showed that most teachers recognized that students experience difficulties with heat and temperature, yet their interpretations of students’ alternative conceptions lacked depth. This pattern is consistent with previous studies indicating that teachers often describe student errors in broad terms and seldom explore their conceptual origins. Overall, these findings suggest important gaps in teachers’ ability to diagnose the conceptual roots of students’ thinking about heat and internal energy. References Baumert, J., & Kunter, M. (2013). The COACTIV model of teachers' professional competence. In M. Kunter, J. Baumert, W. Blum, U. Klusmann, S. Krauss, & M. Neubrand (Eds.), Cognitive activation in the mathematics classroom and professional competence of teachers - Results from the COACTIV project (pp. 25–48). New York: Springer. Chan, K. K. H., Rollnick, M., & Gess-Newsome, J. (2019). A grand rubric for measuring science teachers’ pedagogical content knowledge. In Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 251-269). Springer, Singapore. Coe, R., Aloisi, C., Higgins, S., & Major, L. E. (2014). What makes great teaching? Review of the underpinning research. Duit, R. H., & Treagust, D. F. (2012). Conceptual change: Still a powerful framework for improving the practice of science instruction. In Issues and challenges in science education research: Moving forward (pp. 43-54). Dordrecht: Springer Netherlands. Jasien, P. G., & Oberem, G. E. (2002). Understanding of elementary concepts in heat and temperature among college students and K-12 teachers. Journal of Chemical Education, 79(7), 889. Osborne, R. J., & Cosgrove, M. M. (1983). Children's conceptions of the changes of state of water. Journal of Research in Science Teaching, 20(9), 825-838. Park, S., & Chen, Y-C. (2012). Mapping out the integration of the components of pedagogical content knowledge (PCK) for teaching photosynthesis and heredity. Journal of Research in Science Teaching, 49(7), 922–941. Shulman, L. (1987). Knowledge and teaching: foundations of the new reform. Harvard Educational Review, 57(1), 1–22. Wilson, S. M., Shulman, L. S. & Richert, A. E. (1987). 150 different ways of knowing: Representations of knowledge in teaching. In J. Calderhead (Ed.), Exploring teachers' thinking (pp.104-124). London: Cassess. 10. Teacher Education Research
Paper Predicting Preservice Science Teachers’ AI-TPACK through UTAUT Perceptions and Behavioral Intention Middle East Technical University, Turkey (Türkiye) Presenting Author:Artificial intelligence has rapidly become a significant educational technology with the potential to transform teaching and learning processes. In science education, AI and GenAI tools may support inquiry practices, modeling, scaffolding and adaptive feedback and facilitate access to diverse representations of complex concepts (Crompton & Burke, 2023; Jia et al., 2024). However, these benefits depend on teachers’ capacity to integrate AI meaningfully and responsibly, considering pedagogical fit, scientific accuracy and ethical implications. Research increasingly reports that teachers and preservice teachers demonstrate curiosity toward AI, however they often feel inadequately prepared to use it effectively (Yue et al., 2024). Although AI-TPACK has been introduced as a framework to conceptualize teacher knowledge required for AI integration (Celik, 2023; Ning et al., 2024; Yao, 2021), only measuring PSTs’ AI-TPACK is not sufficient for supporting their professional development. It is also essential to examine factors that may shape the development of AI-TPACK. PSTs’ technology adoption perceptions and their behavioral intention (BI) to use AI can play a critical role in such readiness. The Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al., 2003) provides a well-established framework for explaining why individuals accept and use emerging technologies. UTAUT proposes that performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) influence behavioral intention and usage behavior of new technologies. Although the model was developed in workplace settings, it has been widely adapted and applied in educational research contexts, including AI adoption (An et al., 2023; Chen et al., 2025; Xue et al., 2024). Recent AIEd studies show mixed findings regarding how AI-TPACK relates to UTAUT constructs and BI. Some studies report positive associations suggesting that greater competence supports stronger technology acceptance (An et al., 2023), while others show weak, non-significant or even negative relationships (Parviz & Arthur, 2025; Runge et al., 2025). These mixed results call for further research, particularly among preservice teachers, who are still developing professional competencies and have limited classroom experience. For PSTs, intention may be a stronger indicator of future adoption behavior and a motivational force that encourages further engagement in professional development, which may in turn strengthen competence. Therefore, this study examines UTAUT perceptions and behavioral intention as predictors of preservice science teachers’ AI-TPACK by addressing the following research question: To what extent do preservice science teachers’ UTAUT perceptions and behavioral intention to use AI predict their AI-TPACK in science education? Methodology, Methods, Research Instruments or Sources Used This study employs a quantitative correlational design. The participants were 544 preservice science teachers enrolled in second, third or fourth year of science education programs. First-year PSTs were excluded due to limited coursework exposure relevant to the constructs. The sample included 444 females (81.6%) and 100 males (18.4%), with 159 second-year (29.2%), 215 third-year (39.5%) and 170 fourth-year (31.3%) participants. AI-TPACK was measured using an adapted AI-TPACK Scale developed for preservice science teachers (93 items, eight factors), adapted from MaKinster et al. (2010), Celik (2023) and An et al. (2023). Items were rated on a 5-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5), with higher scores indicating greater perceived competence. Component scores were calculated as the means of their respective items. Behavioral intention and technology acceptance perceptions were measured using a UTAUT-based questionnaire adapted from An et al. (2023) for AI usage in educational contexts. The instrument included five constructs: performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI) and behavioral intention (BI). Items were translated into Turkish and reviewed by bilingual experts to ensure linguistic equivalence. In terms of the factor structure, the adapted scale included the five constructs found in the original scale (PE, EE, FC, SI and BI) (An et al., 2023). To address the research question, correlation analyses and multiple regression analyses were conducted to examine the extent to which UTAUT constructs and behavioral intention explain the variance in AI-TPACK. Conclusions, Expected Outcomes or Findings Correlation analyses indicated that AI-TPACK was significantly and positively correlated with all UTAUT constructs, though strength varied. AI-TPACK was moderately to strongly associated with EE (r = .509, p < .001), PE (r = .418, p < .001) and BI (r = .393, p < .001). Moderate correlation was found with FC (r = .361, p < .001), while SI showed a small but significant correlation (r = .137, p < .001). Multiple regression results indicated that four predictors significantly explained variance in AI-TPACK: EE, FC, PE and BI. The model was statistically significant (Adjusted R² = .391, F(4, 539) = 88.221, p < .001), explaining 39.1% of variance in AI-TPACK. EE was the strongest predictor (β = .360) accounting for 10.5% of the unique variance in AI-TPACK, followed by FC (β = .222), PE (β = .186) and BI (β = .139). SI was not retained in the final model. Findings suggest that perceptions of ease of use (EE) and facilitating conditions (FC) represent key factors for strengthening preservice science teachers’ readiness to integrate AI. In addition, perceived usefulness (PE) and behavioral intention to use AI (BI) contribute uniquely, though to a smaller extent. These results indicate that both motivational perceptions and enabling conditions are associated with PSTs’ AI-TPACK and may inform targeted interventions in teacher education programs, such as practice-based AI integration tasks, infrastructure support, and pedagogically grounded AI training. Notably, prior AIEd studies frequently position AI-TPACK as a predictor of behavioral intention and effort expectancy, whereas the present findings indicate that UTAUT-related perceptions and intention also significantly predict AI-TPACK. This pattern may suggest a mutually reinforcing association between competence and intention, which should be examined through longitudinal or SEM-based designs. References An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468 Chen, S., Huang, L., Shadiev, R., & Hu, P. (2025). An extension of UTAUT model to understand elementary school students’ behavioral intention to use an online homework platform. Education and Information Technologies, 30(1), 229–255. https://doi.org/10.1007/s10639-024-12852-3 Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8 Jia, F., Sun, D., & Looi, C. (2024). Artificial intelligence in science education (2013–2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94–117. https://doi.org/10.1007/s10956-023-10077-6 MaKinster, J. G., Boone, W., & Trautmann, N. M. (2010, March). Development of an instrument to assess science teachers’ perceived technological pedagogical content knowledge. National Association for Research in Science Teaching, Philadelphia, PA. Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978 Parviz, M., & Arthur, F. (2025). Exploring EFL teachers’ behavioral intentions to integrate GenAI applications: Insights from PLS‐SEM and fsQCA. Human Behavior and Emerging Technologies, 2025(1), 5582099. https://doi.org/10.1155/hbe2/5582099 Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of pre-service teachers towards artificial intelligence (AI): The role of AI-related teacher training courses and AI-TPACK within the technology acceptance model. Education Sciences, 15(2), 167. https://doi.org/10.3390/educsci15020167 Venkatesh, Morris, Davis, & Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540 Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT) in higher education: A Systematic review. Sage Open, 14(1). https://doi.org/10.1177/21582440241229570 Yao, Y. (2021). Deep integration of AI and TPACK: Reconstruction of teachers’ knowledge structure in the post-pandemic era. BCP Education & Psychology, 3, 150–154. https://doi.org/10.54691/bcpep.v3i.28 Yue, M., Jong, M. S. Y., & Ng, D. T. K. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29(15), 19505–19536. https://doi.org/10.1007/s10639-024-12621-2 10. Teacher Education Research
Paper Preservice Science Teachers’ AI-TPACK Perceptions Middle East Technical University, Turkey (Türkiye) Presenting Author:Technology is advancing at an immense speed. As technology continued to advance, artificial intelligence (AI) also started to draw interest from education researchers, which gave rise to the field of AI in Education. This field focuses on the application of AI to improve teaching and learning processes with the ultimate goal of transforming education (Crompton & Burke, 2023; Good, 1987) and enhance educational processes. AI tools can support personalized learning by adapting content to individual needs, offering real-time feedback to learners, and it can automate certain administrative and instructional tasks (Alexandrowicz, 2024). In the context of science education, AI tools can assist teachers with modeling complex concepts, simulate experiments and support inquiry-based learning activities (Crompton & Burke, 2023). Several studies indicate that teachers report curiosity and enthusiasm towards AI integration (Alexandrowicz, 2024). Yet, studies show that their competence and confidence in using AI tools remains insufficient (Almuhanna, 2025). Moreover, research with preservice teachers also show similar results, indicating gaps in their awareness, literacy and content and technological knowledge related to AI (Ayanwale et al., 2024). As future educators, preservice teachers play a significant role in mediating between AI tools and learners and must be competent in this process. Overall, it becomes evident that teachers, who are the immediate stakeholders in bringing AI into the classrooms, should be adequately prepared to utilize these tools effectively in their teaching and to navigate the challenges AI integration brings. Equally, preservice teachers, as the future teaching workforce, should also be sufficiently competent in applying educational technology in classrooms (Hu et al., 2025). Consequently, their preparedness for AI integration into education can be examined through the widely accepted Technological Pedagogical Content Knowledge (TPACK) framework. TPACK framework is a commonly used model that describes the domains of knowledge teachers need for effectively integrating technology into their classrooms (Mishra & Koehler, 2006). While the TPACK framework is a valuable model for understanding teacher knowledge for technology integration (Celik, 2023), emerging technologies such as AI challenge its boundaries and creates a necessity for the revision of the model (Uyanik Aktulun et al., 2024). AI-TPACK is an extended version of the TPACK framework. As Mishra and Koehler (2006) developed the original TPACK framework by building on Shulman’s (1986) PCK framework based on the necessity of integrating technology component into teacher knowledge, AI-TPACK builds on TPACK as the complexity of AI tools requires a more nuanced understanding of teacher knowledge. AI-TPACK offers this extended understanding, and it aims to measure teachers’ competencies in effectively integrating AI into their teaching. Additionally, it focuses on the interrelations between AI technology, pedagogical methods and content knowledge. This implies that as educators’ knowledge about AI technology improves, their existing knowledge also transforms accordingly. As the future of the educational workforce, it is critical for preservice teachers to be ready to integrate AI tools in their classrooms (Hu et al., 2025). Researchers deem AI literacy indispensable for future educators, since those with higher AI competence are expected to outperform their peers with lower AI competence (Ayanwale et al., 2024). Accordingly, recent studies examine readiness of preservice teachers to address these issues (Ayanwale et al., 2024; Karataş & Ataç, 2025). However, in the context of science education such studies are limited. Therefore, examining the AI-TPACK of preservice science teachers is essential. Accordingly, this study addresses the following research question: What are preservice science teachers’ (PSTs) perceptions of AI-TPACK in science education? Methodology, Methods, Research Instruments or Sources Used This study adopts a quantitative research approach by utilizing survey research design. A total of 544 PSTs enrolled in science education programs in 6 universities participated in this study. Sample of this study consisted of second-, third- or fourth-year PSTs. First-year students were excluded from participation because they had not yet received sufficient theoretical understanding or experience related to the constructs measured in this study. A convenience sampling strategy was used, taking into account the availability and willingness of students to participate, as well as the cooperation of the institutions and relevant stakeholders. The participants were predominantly female, with 444 females (81.6%) and 100 males (18.4%). Preservice science teachers were distributed across years with 159 in their second year (29.2%), 215 in their third (39.5%) and 170 in the fourth (31.3%). The instrument used in this study to measure the PSTs’ AI-TPACK in science education was specifically adapted for this purpose from three existing instruments developed by MaKinster et al. (2010), Celik (2023) and An et al. (2023) after obtaining permissions from the authors of the original instruments. The participants’ AI-TPACK data were collected with the AI-TPACK Scale. All items were rated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), with higher scores indicating greater self-perceived competence in integrating AI into science education. The adapted AI-TPACK Scale used in the pilot study consisted of 84 items. EFA was conducted during the pilot study and CFA was conducted in the main study to evaluate construct validity, and an eight-factor structure was obtained. The final scale included 93 items across these eight factors: AI technological knowledge (AI-TK, 13 items), content knowledge (CK, 10), pedagogical knowledge (PK, 10), pedagogical content knowledge (PCK, 9), AI technological content knowledge (AI-TCK, 12), AI technological pedagogical knowledge (AI-TPK, 21), AI technological pedagogical content knowledge (AI-TPACK, 13) and AI ethics (AI-ETH, 5). Component scores were calculated as the means of their respective items, and no items were reverse-coded. The phrasing of the items was carefully adapted to the AI and science education context, with an emphasis on clarity and accuracy. Reliability coefficients ranged from acceptable to excellent across the eight components: AI-TK (α = .905), CK (α = .844), PK (α = .890), PCK (α = .886), AI-TCK (α = .894), AI-TPK (α = .936), AI-TPACK (α = .922) and AI-ETH (α = .753). The overall scale reliability was excellent (α = .974). Conclusions, Expected Outcomes or Findings To address the research question, descriptive analysis was conducted for the eight components of the AI-TPACK framework. The participants reported moderately high mean scores across all components. The highest mean scores were found for the pedagogical knowledge (PK) (M = 3.99, SD = 0.47) and the pedagogical content knowledge (PCK) (M = 3.99, SD = 0.42). The lowest mean score was obtained in the AI ethics (AI-ETH) component (M = 3.51, SD = 0.57), followed by the AI technological pedagogical content knowledge (AI-TPACK) component (M = 3.71, SD = 0.52), which indicates slightly lower confidence in PSTs’ integrated competencies and ethical considerations. The descriptive findings revealed that PSTs reported relatively high levels PK and PCK domains which indicated their strong confidence in the pedagogical domains. This pattern was consistent with previous literature (Irmak & Yılmaz-Tüzün, 2019) The lower scores in AI-TPACK and AI-ETH may reflect the new and complex nature of integrating AI into science education. Moreover, the lower score in AI-ETH suggests that while PSTs may feel competent in other areas, their confidence in addressing ethical considerations related to AI integration is comparatively lower. This finding aligns with previous studies such as Karataş and Ataç (2025), which also reported that the ethics component had the lowest scores among teachers and preservice teachers. Overall, findings show that while PSTs generally perceive themselves as having relatively high AI-TPACK levels, there are still gaps in their competence, especially regarding the ethical dimension and the integration of AI across content and pedagogy. The results highlight the need for teacher education programs to address these areas, particularly ethical considerations and holistic AI integration, to ensure future science teachers are adequately prepared to use AI effectively and responsibly in their classrooms. References Alexandrowicz, V. (2024). Artificial intelligence integration in teacher education: Navigating benefits, challenges, and transformative pedagogy. Journal of Education and Learning, 13(6), 346. https://doi.org/10.5539/jel.v13n6p346 Almuhanna, M. A. (2025). Teachers’ perspectives of integrating AI-powered technologies in K-12 education for creating customized learning materials and resources. Education and Information Technologies, 30(8), 10343–10371. https://doi.org/10.1007/s10639-024-13257-y An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179 Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468 Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8 Good, R. (1987). Artificial intelligence and science education. Journal of Research in Science Teaching, 24(4), 325–342. Hu, L., Wang, H., & Xin, Y. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: An empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies, 30(9), 12609–12631. https://doi.org/10.1007/s10639-025-13353-7 Irmak, M., & Yılmaz-Tüzün, Ö. (2019). Investigating pre-service science teachers’ perceived technological pedagogical content knowledge (TPACK) regarding genetics. Research in Science & Technological Education, 37(2), 127–146. https://doi.org/10.1080/02635143.2018.1466778 Karataş, F., & Ataç, B. A. (2025). When TPACK meets artificial intelligence: Analyzing TPACK and AI-TPACK components through structural equation modelling. Education and Information Technologies, 30(7), 8979–9004. https://doi.org/10.1007/s10639-024-13164-2 MaKinster, J. G., Boone, W., & Trautmann, N. M. (2010, March). Development of an instrument to assess science teachers’ perceived technological pedagogical content knowledge. National Association for Research in Science Teaching, Philadelphia, PA. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4. https://doi.org/10.2307/1175860 Uyanik Aktulun, O., Kasapoglu, K., & Aydogdu, B. (2024). Comparing Turkish pre-service STEM and Non-STEM teachers’ attitudes and anxiety toward artificial intelligence. Journal of Baltic Science Education, 23(5), 950–963. https://doi.org/10.33225/jbse/24.23.950 | ||