Development of Artificial Intelligence Awareness Scale (AIAS): Validity and Reliability Study

A. Mücahid Zengin; M. Fatih Turanalp; Faruk Yazar; Hakan Kurt

doi:10.15869/itobiad.1859351

Research Article

Year 2026, Volume: 15 Issue: 1 , 181 - 202 , 31.03.2026

A. Mücahid Zengin , M. Fatih Turanalp , Faruk Yazar , Hakan Kurt

https://doi.org/10.15869/itobiad.1859351

https://izlik.org/JA27AE63GS

Abstract

Project Number

24SBA21001

References

Ahmadi Fatalaki, J., Saeedi, Z., Sheikhzadeh Marand, E., & Nasiri Firuz, A. R. (2024). Artificial Intelligence in Education and Efl Teachers’ Attitudes: Scale Development and Validation. Elsevier BV. https://doi.org/10.2139/ssrn.4687460
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2024). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2025). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
Belanger, A. (2025, June 24). Key fair use ruling clarifies when books can be used for AI training. Ars Technica. https://arstechnica.com/tech-policy/2025/06/key-fair-use-ruling-clarifies-when-books-can-be-used-for-ai-training/
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). IRobot: Teaching the Basics of Artificial Intelligence in High Schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9864
Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı. (4th edn). Pegem Akademi.
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00418-7
Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2024). Explicating AI Literacy of Employees at Digital Workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/tem.2021.3138503
Chen, M. (2023, December 6). What is AI model training and why is it important? https://www.oracle.com/artificial-intelligence/ai-model-training/
Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik. Pegem Akademi Yayınları.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th edn). Sage.
Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
Derico, B. (2025, July 12). Federal judge says voiceover artists AI lawsuit can move forward. https://www.bbc.com/news/articles/cedgzj8z1wjo
Dimitrieska, S. (2024). Generative Artificial Intelligence and Advertising. Trends in Economics, Finance and Management Journal, 6(1), 23–34. https://doi.org/10.69648/eyzi2281
Elliott, D., & Soifer, E. (2022). AI Technologies, Privacy, and Security. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826737
Emsley, R. (2023). ChatGPT: These are not hallucinations – they’re fabrications and falsifications. Schizophrenia, 9(1). https://doi.org/10.1038/s41537-023-00379-4
Erdemir, N., & Atik, S. (2025). Validity and Reliability Analysis of the Artificial Intelligence–Digital Life Balance Scale. Psychiatric Quarterly. https://doi.org/10.1007/s11126-025-10167-1
Ferikoğlu, D., & Akgün, E. (2022). An Investigation of Teachers’ Artificial Intelligence Awareness: A Scale Development Study. Malaysian Online Journal of Educational Technology, 10(3), 215–231. https://doi.org/10.52380/mojet.2022.10.3.407
Field, A. P. (2009). Discovering Statistics Using Spss. Sage Publications.
Gokcearslan, S., Durak, H. Y., Gunbatar, M. S., Uslu, N. A., & Elci, N. (2024). Generative Artificial Intelligence (GenAI) Literacy Scale: Validity and Reliability.
Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base—Analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Jang, C. (2024). Coping with vulnerability: The effect of trust in AI and privacy-protective behaviour on the use of AI-based services. Behaviour & Information Technology, 43(11), 2388–2400. https://doi.org/10.1080/0144929x.2023.2246590
Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User’s reference guide. Scientific Software International.
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. 2016 IEEE Frontiers in Education Conference (FIE), 1–9. https://doi.org/10.1109/fie.2016.7757570
Karasar, N. (2011). Bilimsel Araştırma Yöntemi. Nobel Yay.
Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Sage Publications.
Kong, S.-C., Man-Yin Cheung, W., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
Korseberg, L., & Elken, M. (2025). Waiting for the revolution: How higher education institutions initially responded to ChatGPT. Higher Education, 89(4), 953–968. https://doi.org/10.1007/s10734-024-01256-4
Li, A., & Wang, S. (2025). Functional attitudes towards artificial intelligence among university students: Development of a scale and influencing factors. Higher Education. https://doi.org/10.1007/s10734-025-01497-x
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Ma, S., & Chen, Z. (2024). The Development and Validation of the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS). IEEE Access, 12, 146419–146429. https://doi.org/10.1109/access.2024.3468378
Maleki, N., Padmanabhan, B., & Dutta, K. (2024). AI Hallucinations: A Misnomer Worth Clarifying (No. arXiv:2401.06796). arXiv. https://doi.org/10.48550/arXiv.2401.06796
Martin, K. D., & Zimmermann, J. (2024). Artificial intelligence and its implications for data privacy. Current Opinion in Psychology, 58, 101829. https://doi.org/10.1016/j.copsyc.2024.101829
McKay, C. (2024, April 17). Using AI for Translation: (When) is it Safe? American Translators Association (ATA). https://www.atanet.org/resources/blog-using-ai-for-translation-when-is-it-safe/
Milmo, D. (2025, June 30). Microsoft says AI system better than doctors at diagnosing complex health conditions. The Guardian. https://www.theguardian.com/technology/2025/jun/30/microsoft-ai-system-better-doctors-diagnosing-health-conditions-research
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E., & Bauer, M. (2024). Artificial intelligence and increasing misinformation. The British Journal of Psychiatry, 224(2), 33–35. https://doi.org/10.1192/bjp.2023.136
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N. (2025). Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. 599–627. https://doi.org/10.1145/3715275.3732039
Ng, A. (2016, November 9). What AI Can and Can’t Do. Harvard Business Review. https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636
Pallant, J. (2007). SPSS survival manual: A step-by-step guide to data analysis using SPSS version 15 (3rd edn.). Open University Press.
Pearson, J. (2025, July 13). Artists fear AI using their talent for free. https://www.bbc.com/news/articles/c78n54vqvlzo
Pinski, M., & Benlian, A. (2023). AI Literacy—Towards Measuring Human Competency in Artificial Intelligence. Proceedings of the Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2023.021
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Year 2026, Volume: 15 Issue: 1 , 181 - 202 , 31.03.2026

A. Mücahid Zengin , M. Fatih Turanalp , Faruk Yazar , Hakan Kurt

https://doi.org/10.15869/itobiad.1859351

https://izlik.org/JA27AE63GS

Abstract

Project Number

24SBA21001

References

Ahmadi Fatalaki, J., Saeedi, Z., Sheikhzadeh Marand, E., & Nasiri Firuz, A. R. (2024). Artificial Intelligence in Education and Efl Teachers’ Attitudes: Scale Development and Validation. Elsevier BV. https://doi.org/10.2139/ssrn.4687460
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2024). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2025). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
Belanger, A. (2025, June 24). Key fair use ruling clarifies when books can be used for AI training. Ars Technica. https://arstechnica.com/tech-policy/2025/06/key-fair-use-ruling-clarifies-when-books-can-be-used-for-ai-training/
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). IRobot: Teaching the Basics of Artificial Intelligence in High Schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9864
Büyüköztürk, Ş. (2010). Sosyal bilimler için veri analizi el kitabı. (4th edn). Pegem Akademi.
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00418-7
Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2024). Explicating AI Literacy of Employees at Digital Workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/tem.2021.3138503
Chen, M. (2023, December 6). What is AI model training and why is it important? https://www.oracle.com/artificial-intelligence/ai-model-training/
Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik. Pegem Akademi Yayınları.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th edn). Sage.
Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
Derico, B. (2025, July 12). Federal judge says voiceover artists AI lawsuit can move forward. https://www.bbc.com/news/articles/cedgzj8z1wjo
Dimitrieska, S. (2024). Generative Artificial Intelligence and Advertising. Trends in Economics, Finance and Management Journal, 6(1), 23–34. https://doi.org/10.69648/eyzi2281
Elliott, D., & Soifer, E. (2022). AI Technologies, Privacy, and Security. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826737
Emsley, R. (2023). ChatGPT: These are not hallucinations – they’re fabrications and falsifications. Schizophrenia, 9(1). https://doi.org/10.1038/s41537-023-00379-4
Erdemir, N., & Atik, S. (2025). Validity and Reliability Analysis of the Artificial Intelligence–Digital Life Balance Scale. Psychiatric Quarterly. https://doi.org/10.1007/s11126-025-10167-1
Ferikoğlu, D., & Akgün, E. (2022). An Investigation of Teachers’ Artificial Intelligence Awareness: A Scale Development Study. Malaysian Online Journal of Educational Technology, 10(3), 215–231. https://doi.org/10.52380/mojet.2022.10.3.407
Field, A. P. (2009). Discovering Statistics Using Spss. Sage Publications.
Gokcearslan, S., Durak, H. Y., Gunbatar, M. S., Uslu, N. A., & Elci, N. (2024). Generative Artificial Intelligence (GenAI) Literacy Scale: Validity and Reliability.
Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base—Analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Jang, C. (2024). Coping with vulnerability: The effect of trust in AI and privacy-protective behaviour on the use of AI-based services. Behaviour & Information Technology, 43(11), 2388–2400. https://doi.org/10.1080/0144929x.2023.2246590
Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: User’s reference guide. Scientific Software International.
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. 2016 IEEE Frontiers in Education Conference (FIE), 1–9. https://doi.org/10.1109/fie.2016.7757570
Karasar, N. (2011). Bilimsel Araştırma Yöntemi. Nobel Yay.
Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Sage Publications.
Kong, S.-C., Man-Yin Cheung, W., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
Korseberg, L., & Elken, M. (2025). Waiting for the revolution: How higher education institutions initially responded to ChatGPT. Higher Education, 89(4), 953–968. https://doi.org/10.1007/s10734-024-01256-4
Li, A., & Wang, S. (2025). Functional attitudes towards artificial intelligence among university students: Development of a scale and influencing factors. Higher Education. https://doi.org/10.1007/s10734-025-01497-x
Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Ma, S., & Chen, Z. (2024). The Development and Validation of the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS). IEEE Access, 12, 146419–146429. https://doi.org/10.1109/access.2024.3468378
Maleki, N., Padmanabhan, B., & Dutta, K. (2024). AI Hallucinations: A Misnomer Worth Clarifying (No. arXiv:2401.06796). arXiv. https://doi.org/10.48550/arXiv.2401.06796
Martin, K. D., & Zimmermann, J. (2024). Artificial intelligence and its implications for data privacy. Current Opinion in Psychology, 58, 101829. https://doi.org/10.1016/j.copsyc.2024.101829
McKay, C. (2024, April 17). Using AI for Translation: (When) is it Safe? American Translators Association (ATA). https://www.atanet.org/resources/blog-using-ai-for-translation-when-is-it-safe/
Milmo, D. (2025, June 30). Microsoft says AI system better than doctors at diagnosing complex health conditions. The Guardian. https://www.theguardian.com/technology/2025/jun/30/microsoft-ai-system-better-doctors-diagnosing-health-conditions-research
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E., & Bauer, M. (2024). Artificial intelligence and increasing misinformation. The British Journal of Psychiatry, 224(2), 33–35. https://doi.org/10.1192/bjp.2023.136
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N. (2025). Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers. 599–627. https://doi.org/10.1145/3715275.3732039
Ng, A. (2016, November 9). What AI Can and Can’t Do. Harvard Business Review. https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636
Pallant, J. (2007). SPSS survival manual: A step-by-step guide to data analysis using SPSS version 15 (3rd edn.). Open University Press.
Pearson, J. (2025, July 13). Artists fear AI using their talent for free. https://www.bbc.com/news/articles/c78n54vqvlzo
Pinski, M., & Benlian, A. (2023). AI Literacy—Towards Measuring Human Competency in Artificial Intelligence. Proceedings of the Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2023.021
Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062
Ramazanoglu, M., & Akın, T. (2025). AI readiness scale for teachers: Development and validation. Education and Information Technologies, 30(6), 6869–6897. https://doi.org/10.1007/s10639-024-13087-y
Reuters. (2025, 05). Musk’s xAI updates Grok chatbot after ‘white genocide’ comments | Reuters. https://www.reuters.com/business/musks-xai-updates-grok-chatbot-after-white-genocide-comments-2025-05-17/
Scantamburlo, T., Cortés, A., Foffano, F., Barrué, C., Distefano, V., Pham, L., & Fabris, A. (2025). Artificial Intelligence Across Europe: A Study on Awareness, Attitude and Trust. IEEE Transactions on Artificial Intelligence, 6(2), 477–490. https://doi.org/10.1109/tai.2024.3461633
Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research, 8(2), 23–74.
Schroeder, S. (2023, March 21). ChatGPT was shut down due to a bug that exposed user chat titles. Mashable. https://mashable.com/article/chatgpt-bug-user-histories
Shin, N., Choi, E., & Chang, S. (2024). Students’ attitudes towards an AI-based interview for university admissions: Scale development and intervention effects. Education and Information Technologies, 29(15), 20055–20076. https://doi.org/10.1007/s10639-024-12649-4
Sümer, N. (2000). Yapısal Eşitlik Modelleri: Temel Kavramlar ve Örnek Uygulamalar. Türk Psikoloji Yazıları, 3(6), 49–74.
Tabachnick, B. G., & Fidell, L. S. (2001). SAS for windows workbook for Tabachnick and Fidell using multivariate statistics. Allyn and Bacon.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th edn). Pearson, Allyn & Bacon.
Tavşancıl, E. (2006). Tutumların Ölçülmesi ve SPSS ile Veri Analizi. Nobel Yayın Dağıtım.
The Guardian. (2025, January 31). The Guardian view on AI and copyright law: Big tech must pay. The Guardian. https://www.theguardian.com/technology/2025/jan/31/the-guardian-view-on-ai-and-copyright-law-big-tech-must-pay
Van Dis, E. A. M., Bollen, J., Zuidema, W., Van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226. https://doi.org/10.1038/d41586-023-00288-7
Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768
Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI Grand Challenges for Education. AI Magazine, 34(4), 66–84. https://doi.org/10.1609/aimag.v34i4.2490
Xia, L., Shen, K., Sun, H., An, X., & Dong, Y. (2025). Developing and validating the student learning agency scale in generative artificial intelligence (AI)-supported contexts. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13137-5
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. https://doi.org/10.1080/10447318.2023.2288730
Yıldırım, A., & Şimşek, H. (2006). Sosyal Bilimlerde Nitel Araştırma Yöntemleri (5th edn). Seçkin Yayıncılık.
Zhang, W., Ning, Y., Yu, N., Yao, D., Wu, H., Wu, J., & Fang, B. (2025). Development and validation of the artificial intelligence self-regulated learning scale (AI-SRLS) for teachers. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13670-x

Year 2026, Volume: 15 Issue: 1 , 181 - 202 , 31.03.2026

A. Mücahid Zengin , M. Fatih Turanalp , Faruk Yazar , Hakan Kurt

https://doi.org/10.15869/itobiad.1859351

https://izlik.org/JA27AE63GS

Abstract

Project Number

24SBA21001

References

Ahmadi Fatalaki, J., Saeedi, Z., Sheikhzadeh Marand, E., & Nasiri Firuz, A. R. (2024). Artificial Intelligence in Education and Efl Teachers’ Attitudes: Scale Development and Validation. Elsevier BV. https://doi.org/10.2139/ssrn.4687460
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2024). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2025). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
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Pinski, M., & Benlian, A. (2023). AI Literacy—Towards Measuring Human Competency in Artificial Intelligence. Proceedings of the Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2023.021
Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062
Ramazanoglu, M., & Akın, T. (2025). AI readiness scale for teachers: Development and validation. Education and Information Technologies, 30(6), 6869–6897. https://doi.org/10.1007/s10639-024-13087-y
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Scantamburlo, T., Cortés, A., Foffano, F., Barrué, C., Distefano, V., Pham, L., & Fabris, A. (2025). Artificial Intelligence Across Europe: A Study on Awareness, Attitude and Trust. IEEE Transactions on Artificial Intelligence, 6(2), 477–490. https://doi.org/10.1109/tai.2024.3461633
Schepman, A., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory Validation and Associations with Personality, Corporate Distrust, and General Trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research, 8(2), 23–74.
Schroeder, S. (2023, March 21). ChatGPT was shut down due to a bug that exposed user chat titles. Mashable. https://mashable.com/article/chatgpt-bug-user-histories
Shin, N., Choi, E., & Chang, S. (2024). Students’ attitudes towards an AI-based interview for university admissions: Scale development and intervention effects. Education and Information Technologies, 29(15), 20055–20076. https://doi.org/10.1007/s10639-024-12649-4
Sümer, N. (2000). Yapısal Eşitlik Modelleri: Temel Kavramlar ve Örnek Uygulamalar. Türk Psikoloji Yazıları, 3(6), 49–74.
Tabachnick, B. G., & Fidell, L. S. (2001). SAS for windows workbook for Tabachnick and Fidell using multivariate statistics. Allyn and Bacon.
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Tavşancıl, E. (2006). Tutumların Ölçülmesi ve SPSS ile Veri Analizi. Nobel Yayın Dağıtım.
The Guardian. (2025, January 31). The Guardian view on AI and copyright law: Big tech must pay. The Guardian. https://www.theguardian.com/technology/2025/jan/31/the-guardian-view-on-ai-and-copyright-law-big-tech-must-pay
Van Dis, E. A. M., Bollen, J., Zuidema, W., Van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226. https://doi.org/10.1038/d41586-023-00288-7
Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768
Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI Grand Challenges for Education. AI Magazine, 34(4), 66–84. https://doi.org/10.1609/aimag.v34i4.2490
Xia, L., Shen, K., Sun, H., An, X., & Dong, Y. (2025). Developing and validating the student learning agency scale in generative artificial intelligence (AI)-supported contexts. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13137-5
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. https://doi.org/10.1080/10447318.2023.2288730
Yıldırım, A., & Şimşek, H. (2006). Sosyal Bilimlerde Nitel Araştırma Yöntemleri (5th edn). Seçkin Yayıncılık.
Zhang, W., Ning, Y., Yu, N., Yao, D., Wu, H., Wu, J., & Fang, B. (2025). Development and validation of the artificial intelligence self-regulated learning scale (AI-SRLS) for teachers. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13670-x

Yapay Zekâ Farkındalığı Ölçeğinin (YZFÖ) Geliştirilmesi: Geçerlik ve Güvenirlik Çalışması

Year 2026, Volume: 15 Issue: 1 , 181 - 202 , 31.03.2026

A. Mücahid Zengin , M. Fatih Turanalp , Faruk Yazar , Hakan Kurt

https://doi.org/10.15869/itobiad.1859351

https://izlik.org/JA27AE63GS

Abstract

Yapay zekâ teknolojilerindeki gelişmeler, eğitim alanı başta olmak üzere birçok sektörde dönüştürücü bir potansiyel taşımaktadır. Bu teknolojilerin eğitimde gösterdiği ivme, üniversite öğrencilerinin farkındalık düzeylerinin incelenmesini öncelikli bir konu haline getirmektedir. Bu bağlamda çalışmada, üniversite öğrencilerinin yapay zekâ farkındalığını değerlendirmek üzere Yapay Zekâ Farkındalık Ölçeği geliştirilmiş ve geçerlik ile güvenirlik analizleri gerçekleştirilmiştir. Yapay zekâ teknolojilerinin hızla yaygınlaşmasına rağmen, öğrencilerin farkındalık düzeylerini güncel gelişmeler ışığında ve teknik bilgisi olmayan kullanıcıları da kapsayacak biçimde ölçen bütüncül bir araca rastlanmamaktadır. Bu eksikliği gidermek amacıyla çalışmada keşfedici ardışık karma yöntem deseni kullanılmıştır. Öncelikle, alanyazın taraması ve uzman görüşleriyle bir ön madde havuzu oluşturulmuş, bu havuz nitel değerlendirmelerle gözden geçirilerek sadeleştirilmiştir. Son hâliyle 20 maddeden oluşan taslak ölçek, sosyal bilimler alanında öğrenim gören 361 üniversite öğrencisine uygulanmıştır. Açımlayıcı faktör analizi (AFA) sonucunda ölçek 11 madde ve iki farklı faktörden oluşacak şekilde yapılandırılmıştır: “Okuryazarlık” (7 madde, faktör yükleri .48 ile .72 arasında) ve “Tehditler” (4 madde, faktör yükleri .56 ile .70 arasında). Doğrulayıcı faktör analizi (DFA) iki faktörlü yapıyı doğrulamış ve kabul edilebilir uyum indeksleri elde edilmiştir (RMSEA = .084, SRMR = .074, GFI = .93). İç tutarlılık güvenirliği, Cronbach alfa = genel ölçek için .64 Okuryazarlık alt boyutu için .67 ve Tehditler alt boyutu için .59; McDonald’s Omega = genel ölçek için .65 Okuryazarlık alt boyutu için .67 ve Tehditler alt boyutu için .61 olarak bulunmuş, dört haftalık test-tekrar test uygulamasında yüksek derecede stabilite (r = .80) gözlenmiştir. Sonuçlar ölçeğin geçerlik ve güvenirlik testlerinin güçlü olduğunu göstermekler birlikte, özellikle eğitim politikalarının geliştirilmesi ve bu politika kararlarının şekillendirilmesinde önemli bir kaynak olarak değerlendirilebilir.

Keywords

Yapay Zekâ , Yapay Zekâ Farkındalığı , Yapay Zekâ Okuryazarlığı , Yapay Zekâ Tehditleri

Ethical Statement

Bu araştırma, Necmettin Erbakan Üniversitesi Sosyal ve Beşerî Bilimler Bilimsel Araştırmalar Etik Kurulu'ndan alınan etik onay ile gerçekleştirilmiştir (27 Aralık 2024, Toplantı No: 24, Karar No: 2024/930).

Supporting Institution

Necmettin Erbakan Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından 24SBA21001 numaralı proje kapsamında desteklenmiştir.

Project Number

24SBA21001

References

Ahmadi Fatalaki, J., Saeedi, Z., Sheikhzadeh Marand, E., & Nasiri Firuz, A. R. (2024). Artificial Intelligence in Education and Efl Teachers’ Attitudes: Scale Development and Validation. Elsevier BV. https://doi.org/10.2139/ssrn.4687460
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2024). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2025). Öğretmenler için Yapay Zekâ Tutum Ölçeği: Geçerlik ve Güvenirlik Çalışması. Pamukkale University Journal of Education. https://doi.org/10.9779/pauefd.1418456
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
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Development of Artificial Intelligence Awareness Scale (AIAS): Validity and Reliability Study

Year 2026, Volume: 15 Issue: 1 , 181 - 202 , 31.03.2026

A. Mücahid Zengin , M. Fatih Turanalp , Faruk Yazar , Hakan Kurt

https://doi.org/10.15869/itobiad.1859351

https://izlik.org/JA27AE63GS

Abstract

This study presents the development, validation, and reliability assessment of the Artificial Intelligence Awareness Scale (AIAS), specifically designed to evaluate university students' awareness of artificial intelligence (AI). Despite significant advances in AI technologies and their increasing integration into daily life and educational environments, there is currently no existing measurement instrument that comprehensively evaluates AI awareness among students, incorporates the most recent AI developments, and takes non-technical users into consideration. To address this gap, the present study employed an exploratory sequential mixed-methods design. An initial literature review and expert consultations guided the formation of a preliminary item pool, which was refined through qualitative evaluations. The finalized 20-item draft scale was administered to 361 university students enrolled in social science departments. Exploratory factor analysis (EFA) resulted in an 11-item scale with two distinct factors: "Literacy" (7 items, factor loadings ranging from .48 to .72) and "Threats" (4 items, factor loadings ranging from .56 to .70). Confirmatory factor analysis (CFA) confirmed the two-factor structure, yielding acceptable fit indices (RMSEA = .084, SRMR = .074, GFI = .93). Internal consistency reliability was acceptable (Cronbach's alpha = .64 overall; .67 for Literacy and .59 for Threats; McDonald’s Omega = .65 overall; .67 for Literacy and .61 for Threats), and test-retest stability over a four-week period was high (r = .80). Results demonstrate that AIAS is a valid and reliable instrument for measuring university students' AI awareness. The scale is particularly valuable for identifying specific areas where educational interventions are needed and informing policy decisions.

Keywords: Artificial Intelligence, Artificial Intelligence Awareness, Artificial Intelligence literacy, Artificial Intelligence Threats

Keywords

Artificial Intelligence , Artificial Intelligence Awareness , Artificial Intelligence literacy , Artificial Intelligence Threats

Ethical Statement

The ethical approval required to carry out the study was obtained from the Ethics Committee for Scientific Research in Social and Human Sciences of Necmettin Erbakan University, with permission dated 27 December 2024 (meeting no. 24, decision no. 2024/930).

Supporting Institution

This work has been supported by Necmetttin Erbakan University Scientific Research Projects Coordination Unit under project number 24SBA21001.

Project Number

24SBA21001

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Details

Primary Language	English
Subjects	Communication Technology and Digital Media Studies, Communication and Media Studies (Other)
Journal Section	Research Article
Authors	A. Mücahid Zengin 0000-0002-1976-2748 M. Fatih Turanalp 0000-0001-5752-5615 Faruk Yazar 0000-0002-1663-1371 Hakan Kurt 0000-0002-1826-2397
Project Number	24SBA21001
Submission Date	January 10, 2026
Acceptance Date	March 13, 2026
Publication Date	March 31, 2026
DOI	https://doi.org/10.15869/itobiad.1859351
IZ	https://izlik.org/JA27AE63GS
Published in Issue	Year 2026 Volume: 15 Issue: 1

Cite

APA	Zengin, A. M., Turanalp, M. F., Yazar, F., & Kurt, H. (2026). Development of Artificial Intelligence Awareness Scale (AIAS): Validity and Reliability Study. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 15(1), 181-202. https://doi.org/10.15869/itobiad.1859351

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