Longitudinal relationships between student ethical considerations, behavioral intention, and perceived knowledge in artificial intelligence education
Dados Bibliográficos
| ID | 21538024 |
|---|---|
| Autores | Weipeng Shen (0000-0002-1168-5995, Chinese University of Hong Kong, autor correspondente), Ching Sing Chai (0000-0002-6298-4813, Chinese University of Hong Kong), Thomas K F Chiu (0000-0003-2887-5477, Chinese University of Hong Kong), King Woon Yau (0000-0003-1469-5117, Chinese University of Hong Kong), Helen Meng (0000-0002-4427-3532, Chinese University of Hong Kong), Irwin King (0000-0001-8106-6447, Chinese University of Hong Kong), Savio Wong (0000-0003-2854-5830, Chinese University of Hong Kong), Yeung Yam (0000-0001-9950-5794, Chinese University of Hong Kong) |
| Ano | 2026 |
| Volume | 249 |
| Páginas | 105614 |
| Data de publicação | 2026-08-01 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Computers & Education (JOURNAL) |
| Identificadores do periódico | ISSN: 0360-1315 • E-ISSN: 1873-782X |
| Editora | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.compedu.2026.105614 |
| OpenAlex | W7134165396 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 51 |
Artificial intelligence (AI) has revolutionized various aspects of people’s lives, yet its misuse poses risks to society. When introducing AI into K-12 education, the concept of AI for social good (AISG) is crucial, as it orients students toward the ethical application of AI. Based partially on the Theory of Planned Behavior (TPB), this study explored how AISG, behavioral intention to learn AI (BIAI), and perceived knowledge of AI (AIPK) associate with one another over three years, utilizing a longitudinal and bidirectional design. Random-intercept cross-lagged panel modeling (RI-CLPM) and three-wave data from 614 middle school students (51% boys) demonstrated the evolution of their AISG, BIAI, and AIPK from 2022 to 2024. The results indicated dynamic transitions of student development in AI education. In the first interval, only AISG positively predicted subsequent AIPK. In the second interval, both BIAI and AIPK were positively associated with later AISG, and a reciprocal relationship emerged between BIAI and AIPK. AISG did not significantly predict BIAI in both intervals. These findings empirically underline the implementation of AI ethics for K-12 students in initial AI education. Furthermore, long-term AI curricula should adapt to students’ focus shift from AI ethics to competence- and motivation-focused learning. AI ethics should be front-loaded in AI education, while later instruction should strengthen motivation and perceived competence. • Longitudinal analysis partially based on the theory of planned behavior. • Students’ perception of applying AI for social good positively predicted later perceived knowledge. • Reciprocal associations between perceived knowledge and learning intention over time. • Dynamic transitions in students’ development within middle school AI education
Applications of artificial intelligence · Curriculum · Longitudinal study · Perception · Reciprocal · Structural equation modeling · Theory of planned behavior · Ethics and Social Impacts of AI · Ethics in Business and Education · Explainable Artificial Intelligence (XAI
The Theory of Planned Behavior
Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students
Persons as Contexts
Artificial intelligence in education
Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students
The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change
A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023)
AI literacy in K-12
Personal and Social-Contextual Factors in K–12 Academic Performance
What are artificial intelligence literacy and competency? A comprehensive framework to support them
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance
From Intentions to Actions
The theory of planned behavior
A critique of the cross-lagged panel model.
Perceived Behavioral Control, Self‐Efficacy, Locus of Control, and the Theory of Planned Behavior 1
How to Design AI for Social Good
Three Extensions of the Random Intercept Cross-Lagged Panel Model
Technology education in early childhood education
Unveiling AI literacy in K-12 education
A scoping review of empirical research on AI literacy assessments
Longitudinal relationships between academic self-control and achievement motivation during different adolescence stages
Revealing dynamic relations between mathematics self-concept and perceived achievement from lesson to lesson
Design and validation of the AI literacy questionnaire
Keeping an insecure career under control
In need of opportunities
Investing in AI for social good
Directionality of the relationship between social well-being and subjective well-being
Longitudinal tests of the theory of planned behaviour
Measurement invariance conventions and reporting
A Meta-Analysis
Family environment and self-esteem development
Changes in self-perceptions of competence and intrinsic motivation among elementary schoolchildren
| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 1 |
| Intervalo de citações | 2026 - 2026 (1) |
| Velocidade de citação | current |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |