Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study

Sawanan Dangprasert

doi:10.30935/cedtech/19089

Sawanan Dangprasert ¹ ^*

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CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep681

https://doi.org/10.30935/cedtech/19089

Submitted: 30 May 2025, Published Online: 06 August 2026

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Abstract

The rapid emergence of generative artificial intelligence (GenAI) necessitates a robust and validated framework for digital competency in higher education. This study developed and validated the artificial intelligence-augmented digital competency framework (AIDCF) to address the gap between general digital literacy and artificial intelligence (AI)-specific operational requirements. The research was conducted at a public technological university in Thailand. Employing a developmental mixed-methods design, the primary instrument, a 24-item questionnaire, was reviewed by nine experts, yielding a mean content validity ratio (CVR) of 0.861, with item-level CVRs ranging from 0.778 to 1.000. Exploratory factor analysis with a sample of 300 participants supported a five-factor structure: (1) AI awareness & ethics, (2) prompting & tool proficiency, (3) critical thinking with AI, (4) AI-integrated learning design, and (5) reflective digital practice. These factors collectively explained 65.12% of the total variance, with factor loadings ranging from 0.682 to 0.884 and an overall Cronbach’s alpha of 0.87. The framework’s pedagogical utility was examined through a five-week instructional intervention utilizing the collecting, reviewing, analyzing, framing, and tailoring (CRAFT) model with 36 graduate students. Results revealed high levels of perceived competency (mean = 4.51, standard deviation = 0.53) and qualitative evidence of metacognitive development. The AIDCF provides a theoretically grounded and empirically validated roadmap for fostering advanced AI literacy in the GenAI era.

Keywords: AI literacy, digital competency, critical thinking with AI, technology integration, exploratory factor analysis, CRAFT framework

References

Ayre, C., & Scally, A. J. (2014). Critical values for Lawshe’s content validity ratio: Revisiting the original methods of calculation. Measurement and Evaluation in Counseling and Development, 47(1), 79-86. https://doi.org/10.1177/0748175613513808
Barboutidis, G., & Stiakakis, E. (2023). Identifying the factors to enhance digital competence of students at vocational training institutes. Technology, Knowledge and Learning, 28(2), 613-650. https://doi.org/10.1007/s10758-023-09641-1
Chan, M. K. Y. (2022). Trend of education research in pre and post COVID-19 pandemic. Problems of Education in the 21^st Century, 80(2), 238-241. https://doi.org/10.33225/pec/22.80.238
Crompton, H., & Burke, D. (2024). The nexus of ISTE standards and academic progress: A mapping analysis of empirical studies. TechTrends, 68(4), 711-722. https://doi.org/10.1007/s11528-024-00973-y
Dangprasert, S. (2025). AI-augmented pedagogical framework for superior literature review proficiency. Journal of Theoretical and Applied Information Technology, 103(7), 2750-2762.
Dewey, J. (1986). Experience and education. The Educational Forum, 50(3), 241-252. https://doi.org/10.1080/00131728609335764
Duchatelet, D., Cornelissen, F., & Volman, M. (2024). Features of experiential learning environments in relation to generic learning outcomes in higher education: A scoping review. Journal of Experiential Education, 47(3), 400-423. https://doi.org/10.1177/10538259231211537
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Elkot, M. A., Youssif, E., Hago Elmahdi, O. E., AbdAlgane, M., & Ali, R. (2025). Generative conversational AI: Active practices for fostering students with mild intellectual disabilities to improve English communication skills. Contemporary Educational Technology, 17(1), Article ep549. https://doi.org/10.30935/cedtech/15688
Facione, P. A. (2020). Critical thinking: What it is and why it counts. Insight Assessment. https://www.insightassessment.com/wp-content/uploads/ia/pdf/whatwhy.pdf
Finch, W. H., & French, B. F. (2023). Effect sizes for estimating differential item functioning influence at the test level. Psych, 5(1), 133-147. https://doi.org/10.3390/psych5010013
Floridi, L. (2023). AI as agency without intelligence: On ChatGPT, large language models, and other generative models. Philosophy & Technology, 36, Article 15. https://doi.org/10.1007/s13347-023-00621-y
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson.
ISTE. (2016). ISTE standards for students. International Society for Technology in Education. https://www.iste.org/standards/for-students
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). ACM. https://doi.org/10.1145/3313831.3376727
Miettinen, R. (2000). The concept of experiential learning and John Dewey’s theory of reflective thought and action. International Journal of Lifelong Education, 19(1), 54-72. https://doi.org/10.1080/026013700293458
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509. https://doi.org/10.1002/pra2.487
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Sidiropoulos, D., & Anagnostopoulos, C.-N. (2024). Applications, challenges and ethical issues of AI and ChatGPT in education. arXiv. https://doi.org/10.48550/arXiv.2402.07907
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
U-senyang, S. (2024). Experiential learning in action: Analyzing outcomes and educational implications. Journal of Education and Learning Reviews, 1(2), 13-28. https://doi.org/10.60027/jelr.2024.771
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens. Publications Office of the European Union. https://doi.org/10.2760/115376
Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: Factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1354929
Žerovnik, A. (2024). Technology-enhanced feedback system usability in the context of self-regulation promotion. Education Sciences, 14(9), Article 948. https://doi.org/10.3390/educsci14090948
Zhang, Z., Maeda, Y., Newby, T., Cheng, Z., & Xu, Q. (2023). The effect of preservice teachers’ ICT integration self-efficacy beliefs on their ICT competencies: The mediating role of online self-regulated learning strategies. Computers & Education, 193, Article 104673. https://doi.org/10.1016/j.compedu.2022.104673

Citation

Dangprasert, S. (2026). Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study. Contemporary Educational Technology, 18(3), Article ep681. https://doi.org/10.30935/cedtech/19089

Dangprasert, S. (2026). Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study. Contemporary Educational Technology, 18(3), ep681. https://doi.org/10.30935/cedtech/19089

Dangprasert S. Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study. CONT ED TECHNOLOGY. 2026;18(3), ep681. https://doi.org/10.30935/cedtech/19089

Dangprasert, Sawanan. "Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study". Contemporary Educational Technology 2026 18 no. 3 (2026): ep681. https://doi.org/10.30935/cedtech/19089

Dangprasert, Sawanan "Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study". Contemporary Educational Technology, vol. 18, no. 3, 2026, ep681. https://doi.org/10.30935/cedtech/19089

Dangprasert S. Designing and validating a digital competency framework for AI-augmented learning: An exploratory factor analysis study. CONT ED TECHNOLOGY. 2026;18(3):ep681. https://doi.org/10.30935/cedtech/19089