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.
| Primary Language | English |
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| Subjects | Lifelong learning |
| Journal Section | Research Article |
| Authors | |
| Submission Date | October 30, 2025 |
| Acceptance Date | December 23, 2025 |
| Publication Date | December 31, 2025 |
| Published in Issue | Year 2025 Volume: 7 Issue: 2 |