Aug 2026· Frontiers in Education· Vol 11· 0 citations· 77 references
TL;DR
Examining undergraduate students’ AI disclosure practices suggests that institutional AI disclosure frameworks may benefit from addressing both policy compliance and the affective and disciplinary dimensions of students’ SRL decision-making.
Abstract
As generative artificial intelligence (GAI) tools become popular in education, questions about when and whether students disclose their AI use have emerged as a critical concern for academic integrity and self-regulated learning (SRL). Using SRL as an interpretive lens, we examine undergraduate students’ AI disclosure practices, investigating how their worries about negative consequences, AI usage behaviours, and demographic factors relate to their willingness to disclose AI use in academic settings. Drawing on survey data from 78 undergraduate students enrolled in an online education course, we developed study-specific survey items addressing worry, disclosure, and AI use. Given the developmental status of the instrument, we retained two preliminary composite indicators, worry and disclosure to teachers, and analyzed less internally consistent peer-disclosure and AI-use items individually. Our findings reveal a pattern of co-occurring worries: fear of teacher judgment and fear of social stigmatization are strongly correlated, and both are associated with students’ active concealment of AI use. Interpretively, we describe two contrasting response patterns among students: a transparent-disclosure pattern, in which disclosing AI use to instructors appears to reflect part of SRL processes, and an anxious-concealment pattern, in which heavier AI users report less transparency with teachers. A further finding is that anxiety is not associated with reduced disclosure to teachers directly, but is associated with increased peer-only sharing and complete secrecy. Exploratory analyses also suggest demographic patterns worth further study, with academic discipline showing the largest association with disclosure and a marginal indication that monolingual students may disclose less than multilingual students. Given the sample size, we reserve our interpretations and treat these as hypotheses for future work rather than established effects. These findings suggest that institutional AI disclosure frameworks may benefit from addressing both policy compliance and the affective and disciplinary dimensions of students’ SRL decision-making.
Artificial intelligence (AI) is reshaping the landscape of higher education, yet empirical understanding of how university instructors perceive, engage with, and evaluate AI in their teaching contexts remains limited. This study examines university instructors' general perceptions, institutional AI engagement, student experience impact, concerns about AI, and future expectations regarding artificial intelligence in higher education, and investigates whether these dimensions differ by demographic and professional characteristics and which factors predict institutional AI engagement. A quantitative cross-sectional survey design was employed. Data were collected from N = 229 university instructors across five academic schools using the QUIA instrument with five subscales of five items each — General Perceptions (GPI), Current AI Use (UAI), Student Experience Impact (ISE), Concerns About AI (CAU), and Future Expectations (EAI) — scored on a five-point Likert scale. Instructors expressed positive agreement across all five dimensions. Future Expectations (EAI) were highest and Current AI Use (UAI) lowest, revealing an attitude-engagement gap. Concern about AI replacing teachers was the only item below the neutral midpoint. Female instructors reported significantly higher Concerns About AI (CAU) than male colleagues. School of Education instructors reported the highest institutional AI engagement. Student Experience Impact (ISE) and General Perceptions (GPI) were the primary psychological predictors of institutional AI engagement, while gender and School of Education affiliation were the significant demographic predictors. Institutions seeking to increase responsible AI adoption should prioritize structured, discipline-specific professional development that enables instructors to experience AI's pedagogical benefits directly.
Fatima Khalifeh, Raúl Santiago, Ramon Palau· International journal of res...· 0 citations
It is argued that AI self-efficacy, characterized as a student’s confidence in their capacity to employ AI ethically and efficiently, constitutes a substantial determinant and the focus of instruction should transition from a “one-size-fits-all” approach to tailored interventions designed to enhance student empowerment.
H. Herianto, Eko Wahyudi, Andi Jusmiana et al.· Knowledge Management & E...· 0 citations
This study examines the lived experiences of senior high school students who were falsely accused by their teachers of using Artificial Intelligence (AI) to produce academic work in a private Catholic school in Tagum City, Davao del Norte. To gain a deeper understanding of this phenomenon, the study employed a qualitative research design combined with a phenomenological approach. Data were gathered through focused group discussions (FGD) and in-depth interviews (IDI) involving twelve (12) selected senior high school students who had experienced teacher suspicions of AI use. Through thematic analysis, the findings revealed three major dimensions of students’ experiences: lived experiences, coping mechanisms, and insights regarding teacher suspicions of AI-generated work. Participants reported experiencing emotional distress, including overthinking and low confidence, following the false accusations. Additionally, they also described how advanced writing skills were sometimes misinterpreted as indicators of AI-generated content. To cope with the challenges, students validated their academic efforts through evidence of authorship, communicated strategically with teachers, and adapted their writing practices. Furthermore, participants reflected on the importance of transparency and accountability in their work, while some viewed the experience as an affirmation of their writing competence. Collectively, the study's findings demonstrate the significance of ethical practices in AI detection, transparency, and positive learning conditions to secure the rights, performance, and well-being of the students.
Noelle Sheenan Namocot, Heinrich Brynnt Carmel Viado, Sharlene Gwen Sayon et al.· Psychology and Education: A...· 0 citations
Existing AI-literacy measures often emphasize broad knowledge, attitudes, application, or ethical awareness rather than how learners use AI within self-regulated academic learning. This study developed and validated the Multidimensional Aspects of AI Literacy Scale for Pre-Service Teachers (MAILS-PT), which assesses Ethical Use, Metacognitive Monitoring (Monitor), Motivational Regulation (Motivate), Behavioral Regulation (Regulate), and Critical Evaluation (Evaluate).
An initial 60-item pool was reduced to 40 items through expert content review. The scale was administered to 420 pre-service teachers recruited in Spring 2025 for exploratory factor analysis and to a separately recruited sample of 684 in Fall 2025 for confirmatory and validation analyses. Ordinal-data methods, reliability analysis, assessment of convergent and discriminant validity, and gender measurement-invariance testing were conducted. A 15-item AI-Assisted Learning Judgment Quiz was used to examine preliminary concurrent associations with a framework-aligned judgment measure.
Exploratory factor analysis retained 30 items, six per dimension; the five-factor solution explained 58.3% of the variance, with primary loadings ranging from 0.639–0.829. Confirmatory factor analysis supported the structure,
χ
2
(395) = 522.90,
p
< 0.001, CFI = 0.988, TLI = 0.986, RMSEA = 0.022, 90% CI [0.016, 0.027], SRMR = 0.037. Subscale omega values ranged from 0.851–0.889 in the exploratory sample and 0.848–0.874 in the confirmatory sample. AVE values ranged from 0.522–0.583 and HTMT values from 0.029–0.272. Configural, loading, and threshold invariance across gender were supported under the specific ordered-categorical JASP/lavaan parameterization. Ethical Use showed near-zero relationships with the other dimensions. The five subscales jointly explained 41.3% of judgment-quiz variance,
R
= 0.643,
R
2
= 0.413, adjusted
R
2
= 0.409,
F
(5, 678) = 95.48,
p
< 0.001.
MAILS-PT provides preliminary psychometric support for five distinguishable aspects of learning-related AI literacy among pre-service teachers. Subscales should be interpreted separately, with Ethical Use treated as a distinct yet related domain of responsible academic AI use. Quiz associations provide preliminary framework-aligned concurrent evidence, not independent criterion or predictive validity. Further validation across institutions, cultures, and behavioral or longitudinal criteria is needed.
Ismet Kaya, Ruşen Meylani· Frontiers in Psychology· 0 citations
Artificial intelligence (AI) is rapidly being integrated into university curricula, yet quantitative indicators of AI use reveal little about how learners use AI as a learning resource or what educational outcomes follow. This cross-sectional survey study of 212 university students enrolled in AI-integrated courses examined the associations of AI literacy, instructor feedback, and two single-item AI use indicators—the proportion of in-class AI use and total weekly AI use time—with self-perceived cognitive learning outcomes, measured across the six cognitive processes of the revised Bloom’s taxonomy. Confirmatory factor analyses supported multidimensional and higher-order structures, but the cognitive domains overlapped substantially (interfactor correlations up to 0.943; HTMT up to 0.946), so domain-level distinctions should be interpreted with caution. A regression model with the four predictors explained 46.3% of the variance in overall self-perceived cognitive learning outcomes (R2 = 0.463, adjusted R2 = 0.452). When all predictors were considered simultaneously, only AI literacy showed a significant positive association (B = 0.615, β = 0.593, 95% CI [0.475, 0.754], p < 0.001); the data did not provide evidence for independent associations of instructor feedback or the two AI use indicators, whose weaker associations may partly reflect their single-item measurement. AI literacy remained significantly associated with all six cognitive domains after Benjamini–Hochberg correction. These findings suggest—within the limits of a cross-sectional, self-report design—that the quantity of AI use and learners’ competency to understand, evaluate, and self-regulate AI use are empirically distinct indicators that universities should measure separately.
Results indicate a significant level of AI usage among students, primarily for content management and academic support and the importance of integrating ethical considerations and promoting digital literacy in AI-enabled learning environments.
Christina Costa, Martha Almendarez Langland, Jason Roberson et al.· The Journal of Scholarship o...· 0 citations