Research Article

Development and Validation of the Lifelong Artificial Intelligence Ethical Awareness Scale (LAIEAS)

Volume: 7 Number: 2 December 31, 2025

Development and Validation of the Lifelong Artificial Intelligence Ethical Awareness Scale (LAIEAS)

Abstract

This study aimed to develop and validate the Lifelong Ethical Awareness in Artificial Intelligence Scale (LAIEAS), designed to measure individuals’ ethical awareness toward artificial intelligence (AI) technologies within the framework of lifelong learning. The research followed a methodological design, including item pool generation, expert evaluation, pilot testing, exploratory and confirmatory factor analyses, and reliability-validity assessments. Data were collected online from two independent samples: a pilot group of 200 participants for Exploratory Factor Analysis (EFA) and a confirmatory group of 472 participants for Confirmatory Factor Analysis (CFA). The initial 60-item pool was refined to a 26-item final form after excluding low-loading and cross-loading items. EFA results revealed a five-factor structure Awareness, Values/Attitude, Behavioral Intention, Critical Evaluation, and Lifelong Learning/Adaptation explaining 82.4% of the total variance (KMO = .931, Bartlett’s χ²(1770) = 6214.54, p < .001). CFA results confirmed the model’s adequacy with excellent fit indices (χ²/df = 2.47, CFI = .962, TLI = .953, RMSEA = .049, SRMR = .041). Reliability coefficients were high across all dimensions (Cronbach’s α ≥ .86), and validity analyses supported the convergent, discriminant, and criterion validity of the scale (AVE = .65-.72, HTMT < .85). The test–retest reliability over a three week interval yielded r = .89 (p < .001). The findings indicate that AIEAS is a psychometrically sound and theoretically grounded instrument for assessing individuals’ ethical awareness, values, and behaviors concerning AI technologies. The scale highlights that ethical awareness is not a static trait but a dynamic and lifelong competency integrating cognitive, affective, and behavioral components. Therefore, LAIEAS provides a valid and reliable tool for educational, institutional, and policy contexts to evaluate and promote ethical consciousness in the age of artificial intelligence.

Keywords

lifelong ethic , ethical awareness , artificial intelligence , scale development

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Arslankara, V. B. (2025). Artificial intelligence age communication in the context of instrumental rationality. Journal of Continuous Vocational Education and Training, 8(1), 98-114.
  3. Arslankara, V. B., & Usta, E. (2024). Generative artificial intelligence as a lifelong learning self efficacy: Usage and competence scale. Journal of Teacher Education and Lifelong Learning, 6(2), 288-302. https://doi.org/10.51535/tell.1489304
  4. Arslankara, V. B., & Usta, E. (2025). Yapay zeka her dilde aynı mı konuşur? üretken yapay zeka yanıtlarının dile göre farklılaşması üzerine bir inceleme. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, 7(2), 623-641.
  5. Boddington, P. (2017). Towards a code of ethics for artificial intelligence. Cham: Springer. https://doi.org/10.1007/978-3-31960648-4
  6. Candy, P. C. (2002). Lifelong learning and information literacy. White House Conference on School Libraries. Washington, DC: U.S. National Commission on Libraries and Information Science.
  7. Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  8. Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
  9. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). London: Sage. Floridi, L. (2019). The logic of information: A theory of philosophy as conceptual design. Oxford: Oxford University Press. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
  10. Gert, B. (2004). Common morality: Deciding what to do. New York: Oxford University Press.
APA
Arslankara, V. B., & Usta, E. (2025). Development and Validation of the Lifelong Artificial Intelligence Ethical Awareness Scale (LAIEAS). Journal of Teacher Education and Lifelong Learning, 7(2), 319-329. https://doi.org/10.51535/tell.1813310