Research Article

Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education

Volume: 11 Number: 4 October 12, 2026

Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education

Abstract

As generative artificial intelligence (GenAI) becomes increasingly embedded in online and blended higher education, pressing assumptions about authorship, academic integrity, and transparency are being re-examined, raising urgent questions for the transformation of the teaching and learning environment. While existing studies document students’ attitudes toward AI and general AI literacy, fewer instruments assess students’ self-reported understanding of responsible academic AI use. This study reports the development and preliminary validation of the Responsible AI Use Knowledge Scale (RAUKS) for college students in teacher education. Guided conceptually by the 6Cs approach as an item-development heuristic, an initial 46-item pool was administered to 410 undergraduate students. Exploratory structural equation modeling (ESEM) supported a refined 24-item instrument with a three-factor structure: Ethical Normative Knowledge (ENK), Critical Corrective Knowledge (CCK), and Procedural Disclosure Knowledge (PDK). The three-factor solution explained 61.67% of the total variance and demonstrated excellent model fit (χ² (230) = 364, p < .001, RMSEA = .038, SRMR = .033, CFI = .999, TLI = .999). Internal consistency reliability was strong across factors (ω = .97 for ENK, .84 for CCK, and .81 for PDK). The results provide initial evidence that responsible AI use perceived knowledge is multidimensional, comprising integrity-related expectations, critical evaluation of AI outputs, and disclosure procedures. RAUKS contributes a knowledge-focused self-report assessment tool offering practical utility for diagnosing student preparedness, informing instructional design, and supporting integrity-oriented AI policies. Further cross-validation across institutions, programs, and modalities is recommended.

Keywords

Responsible AI use, Generative AI, Teacher education, Scale development, AI literacy

Supporting Institution

This research received no external funding.

Project Number

REC Code 2025-167

Ethical Statement

The authors declare no conflict of interest. For ethical clearance, the study received approval from the Philippine Normal University Research Ethics Board (REC Code 2025-167). As part of declarations of the paper: Generative artificial intelligence tools were used in a limited and transparent manner during manuscript preparation. Specifically, ChatGPT (version 5.2) was utilized to support language editing, structural refinement, and clarity of expression. While AI tools supported aspects of editorial refinement, all substantive decisions regarding the study’s design, data analysis, interpretation, and final content in its current form were made by the authors. The use of this tool complemented, but did not replace, the authors’ scholarly judgment and manual review.

Thanks

The authors gratefully acknowledge the expert contributions of the reviewers who provided substantive feedback during the instrument development and content validation processes, particularly the faculty members from the Philippine Normal University–Manila, Philippine Normal University–South Luzon Campus, Philippine Normal University–North Luzon Campus, and De La Salle–College of Saint Benilde, Manila.

References

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APA
Cacho, R., Cardona, R., Amora, J., & Dejapa, R. (2026). Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education. Journal of Learning and Teaching in Digital Age, 11(4), 400-415. https://doi.org/10.53850/joltida.1895088
AMA
1.Cacho R, Cardona R, Amora J, Dejapa R. Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education. JOLTIDA. 2026;11(4):400-415. doi:10.53850/joltida.1895088
Chicago
Cacho, Reynald, Roldan Cardona, Johnny Amora, and Ryan Dejapa. 2026. “Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure Among College Students in Teacher Education”. Journal of Learning and Teaching in Digital Age 11 (4): 400-415. https://doi.org/10.53850/joltida.1895088.
EndNote
Cacho R, Cardona R, Amora J, Dejapa R (October 1, 2026) Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education. Journal of Learning and Teaching in Digital Age 11 4 400–415.
IEEE
[1]R. Cacho, R. Cardona, J. Amora, and R. Dejapa, “Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education”, JOLTIDA, vol. 11, no. 4, pp. 400–415, Oct. 2026, doi: 10.53850/joltida.1895088.
ISNAD
Cacho, Reynald - Cardona, Roldan - Amora, Johnny - Dejapa, Ryan. “Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure Among College Students in Teacher Education”. Journal of Learning and Teaching in Digital Age 11/4 (October 1, 2026): 400-415. https://doi.org/10.53850/joltida.1895088.
JAMA
1.Cacho R, Cardona R, Amora J, Dejapa R. Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education. JOLTIDA. 2026;11:400–415.
MLA
Cacho, Reynald, et al. “Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure Among College Students in Teacher Education”. Journal of Learning and Teaching in Digital Age, vol. 11, no. 4, Oct. 2026, pp. 400-15, doi:10.53850/joltida.1895088.
Vancouver
1.Reynald Cacho, Roldan Cardona, Johnny Amora, Ryan Dejapa. Developing and Examining the Responsible AI Use Knowledge Scale (RAUKS): Initial Evidence of a Three-Factor Structure among College Students in Teacher Education. JOLTIDA. 2026 Oct. 1;11(4):400-15. doi:10.53850/joltida.1895088