Behavioral Sciences

Research

Jump to: Other

31 pages, 830 KB

Open AccessArticle

Research on the Impact of Generative Artificial Intelligence Usage Behavior on the Learning Outcomes of Higher Vocational Students

by Yafeng Song, Kangjian Zhao, Li Li and Wei Dong

Behav. Sci. 2026, 16(7), 1166; https://doi.org/10.3390/bs16071166 - 10 Jul 2026

Abstract

Generative Artificial Intelligence (GenAI) has been increasingly integrated into vocational education teaching and students’ learning. Thus, instructing higher vocational students to use GenAI effectively and improving their self-reported perceptions of learning outcomes are critical. Based on the talent development requirements of vocational education, [...] Read more.

Generative Artificial Intelligence (GenAI) has been increasingly integrated into vocational education teaching and students’ learning. Thus, instructing higher vocational students to use GenAI effectively and improving their self-reported perceptions of learning outcomes are critical. Based on the talent development requirements of vocational education, this study developed and validated the GenAI Usage Behavior Scale and the Higher Vocational Students’ Perceived Learning Outcomes Scale. Subsequently, an empirical analysis was conducted using data from 1110 valid questionnaires collected from Chinese higher vocational students. According to the descriptive statistical analysis, the overall usage behavior of higher vocational students was generally at a medium–high level. They performed relatively well in terms of usage habits, moderately in usage contexts, and showed a relatively low frequency of use. The overall evaluation for higher vocational students’ perceived learning outcomes was rated as above average with competency development ranking highest, followed by skill application and knowledge mastery. As for group differences, the results of the independent samples t-test and one-way analysis of variance (ANOVA) revealed no overall significant differences in perceived learning outcomes across genders. Significant differences were only observed in the skill application dimension across grades, while all dimensions of perceived learning outcomes showed statistically significant disparities across major categories. From the perspective of the correlational mechanism, correlation and regression analyses revealed a statistically significant negative correlation between GenAI usage frequency and perceived learning outcomes. In contrast, usage habits and contexts exhibited statistically significant positive correlations. Among these factors, usage habits demonstrated stronger explanatory power. Accordingly, colleges, teachers, and students each must have clear priorities and work collaboratively to enhance students’ ability to use GenAI effectively, thereby leveraging its supportive role in improving perceived learning outcomes for higher vocational students more efficiently. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

30 pages, 1325 KB

Open AccessArticle

The Six-Facet Artificial Intelligence Literacy Questionnaire (SFAILQ): Assessing AI Literacy in Adolescents, Young Adults, and Midlife Adults

by Qingqi Liu, Wenjiao Miao, Jingjing Li and Yuju Lei

Behav. Sci. 2026, 16(7), 1110; https://doi.org/10.3390/bs16071110 - 3 Jul 2026

Viewed by 254

Abstract

AI literacy has become a pressing concern across disciplines, calling for comprehensive measurement tools applicable to diverse age groups. Building on existing research, we propose a six-facet model encompassing affective experiences, usage skills, cognitive evaluation, ethical norms, responsible use, and self-development. The present [...] Read more.

AI literacy has become a pressing concern across disciplines, calling for comprehensive measurement tools applicable to diverse age groups. Building on existing research, we propose a six-facet model encompassing affective experiences, usage skills, cognitive evaluation, ethical norms, responsible use, and self-development. The present study aimed to develop and validate the Six-Facet Artificial Intelligence Literacy Questionnaire (SFAILQ) among 2443 Chinese participants aged 12 to 60 years, spanning adolescence to middle adulthood, with a disproportionately larger proportion falling within the 18-to-40 age range. An item reduction analysis was conducted using the first split-half sample (N₁ = 1217), and reliability and validity analyses were performed with the second split-half sample (N₂ = 1226). The final SFAILQ consists of 32 items assessing six dimensions: affective experiences (5 items), usage skills (5 items), cognitive evaluation (6 items), ethical norms (6 items), responsible use (4 items), and self-development (6 items). All six dimensions and the total score correlated significantly and positively with academic self-efficacy (usage skills showing the strongest correlation) and with academic engagement (responsible use demonstrating the highest correlation). The SFAILQ demonstrated high internal consistency, construct validity, convergent validity, discriminant validity, and criterion-related validity. It may serve as an effective tool for evaluating AI literacy among adolescents, young adults, and midlife adults. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

27 pages, 806 KB

Open AccessFeature PaperArticle

The Effects of Agent Type and Feedback Style on Self-Directed Learning: A Mixed-Methods Study

by Xue Han, Jing Cao, Zi Wang and Heng Luo

Behav. Sci. 2026, 16(7), 1069; https://doi.org/10.3390/bs16071069 - 30 Jun 2026

Viewed by 324

Abstract

This study examines how agent type (customized vs. general-purpose) and feedback style (Socratic vs. directive) are associated with learners’ engagement with artificial intelligence (AI)-generated feedback in self-directed learning (SDL), with particular attention to patterns in feedback quality, self-regulatory behaviors, learning experiences, and learning [...] Read more.

This study examines how agent type (customized vs. general-purpose) and feedback style (Socratic vs. directive) are associated with learners’ engagement with artificial intelligence (AI)-generated feedback in self-directed learning (SDL), with particular attention to patterns in feedback quality, self-regulatory behaviors, learning experiences, and learning outcomes. A 2 × 2 mixed factorial experiment was conducted with 51 postgraduate students who completed two instructional design tasks under different feedback conditions. Quantitative results indicated that customized agents generated feedback with higher accuracy and specificity than general-purpose agents. Socratic feedback was associated with stronger comprehension monitoring, whereas directive feedback was associated with higher cognitive load. A significant interaction suggested that the advantage of customized agents in learning outcomes, operationalized as short-term task improvement, emerged under directive feedback but not under Socratic feedback. Qualitative analysis indicated that Socratic prompts encouraged deeper, logic-oriented reflection, whereas directive feedback provided actionable guidance that facilitated task completion. Learners adopted feedback selectively based on perceived accuracy, and trust in customized agents was higher when feedback was clear and contextually aligned. These findings suggest that the effectiveness of AI-generated feedback is shaped not only by agent type and feedback style but also by how learners evaluate and use feedback. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

21 pages, 660 KB

Open AccessArticle

Using Generative AI in Learning and Students’ Innovative Behavior: A Dual-Path Examination Based on the UTAUT Model

by Lingyi Huang and Wenhao Luo

Behav. Sci. 2026, 16(6), 1002; https://doi.org/10.3390/bs16061002 - 16 Jun 2026

Viewed by 383

Abstract

The rapid development of generative artificial intelligence (GAI) has exerted extensive and far-reaching impacts on college students’ learning, making it a topic worthy of in-depth investigation. This study aims to explore the impact of GAI usage on college students’ innovative learning behaviors, drawing [...] Read more.

The rapid development of generative artificial intelligence (GAI) has exerted extensive and far-reaching impacts on college students’ learning, making it a topic worthy of in-depth investigation. This study aims to explore the impact of GAI usage on college students’ innovative learning behaviors, drawing on the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, the research explores the mediating mechanisms of effort expectancy and performance expectancy, as well as the moderating role of growth mindset in this process. Based on a sample of 430 Chinese college students recruited from diverse academic majors, the proposed moderated mediation model is empirically examined through latent structural equation modeling analysis. The results indicate that using GAI in learning significantly enhances students’ perceptions of effort expectancy and performance expectancy, thereby fostering their subsequent innovative behavior. Notably, the findings reveal that while performance expectancy mediates the relationship between GAI usage and innovative behavior, a growth mindset weakens this indirect pathway. The practical implications of this study are also discussed for both students and universities. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

16 pages, 659 KB

Open AccessArticle

The Mediating Role of Self-Regulation and Artificial Intelligence Awareness in the Effect of Individual Entrepreneurship Tendencies on Learning Agility in High School Students

by Merve Coşgun Demirdağ, Najwa Salem Albeladi, Juan Gómez-Salgado and Murat Yıldırım

Behav. Sci. 2026, 16(6), 973; https://doi.org/10.3390/bs16060973 - 11 Jun 2026

Viewed by 329

Abstract

Learning agility is considered a key competence for adapting to rapidly changing educational and technological environments. Although entrepreneurial tendencies have been associated with adaptive learning outcomes, the psychological mechanisms underlying this relationship remain insufficiently understood. This study examined whether self-regulation and artificial intelligence [...] Read more.

Learning agility is considered a key competence for adapting to rapidly changing educational and technological environments. Although entrepreneurial tendencies have been associated with adaptive learning outcomes, the psychological mechanisms underlying this relationship remain insufficiently understood. This study examined whether self-regulation and artificial intelligence (AI) awareness sequentially mediate the relationship between individual entrepreneurial tendencies and learning agility among high school students. The study involved 564 high school students (55% girls, 45% boys; aged 14–19 years, M = 17.02, SD = 1.28) from two public schools in Türkiye. Participants completed validated measures of entrepreneurial tendencies, self-regulation, AI awareness, and learning agility. The hypothesized serial mediation model was tested using PROCESS Macro Model 6. Entrepreneurial tendencies were positively associated with learning agility both directly and indirectly. Self-regulation emerged as a significant independent mediator, and a significant sequential mediation pathway was identified through self-regulation and AI awareness. The findings suggest that entrepreneurial tendencies are associated with higher levels of self-regulation and AI awareness, which are in turn associated with learning agility. The results highlight the importance of self-regulation and AI awareness as factors associated with the relationship between entrepreneurial tendencies and learning agility. Educational practices that foster entrepreneurship, self-regulation, and AI awareness may support students’ adaptability and readiness for rapidly evolving digital learning environments. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

23 pages, 1111 KB

Open AccessArticle

A Double-Edged Algorithm Attitude: How Appreciation and Aversion Shape Students’ AI Learning Anxiety in Higher Education

by Zhaolin Lu, Jiayuan Guo, Tian Yuan, Yue Zhang, Jiajie Yang, Yuxuan Du, Minghua Chen, Mingyi Xie, Liangyu Xian, Hui Cao and Kexin Zhang

Behav. Sci. 2026, 16(6), 932; https://doi.org/10.3390/bs16060932 - 5 Jun 2026

Viewed by 679

Abstract

Artificial intelligence is rapidly entering higher education, yet many students experience anxiety when learning to use it. This study examines how performance expectations, perceived explainability, and perceived ethical risks shape two algorithm attitudes, algorithm aversion and algorithm appreciation, and how these attitudes influence [...] Read more.

Artificial intelligence is rapidly entering higher education, yet many students experience anxiety when learning to use it. This study examines how performance expectations, perceived explainability, and perceived ethical risks shape two algorithm attitudes, algorithm aversion and algorithm appreciation, and how these attitudes influence artificial intelligence learning anxiety. Using a hybrid partial least squares structural equation modeling–artificial neural network (PLS-SEM–ANN) approach, this study analyzed survey data from 409 university students. Results show that both algorithm aversion and algorithm appreciation significantly increase artificial intelligence learning anxiety, although the effect of algorithm aversion is much stronger, supporting an approach–avoidance account. Perceived ethical risk is the strongest predictor of algorithm aversion but has no significant effect on algorithm appreciation. By contrast, performance expectations and perceived explainability strengthen algorithm appreciation while also showing weaker positive effects on algorithm aversion. These findings suggest that, in educational settings, stronger performance value and greater explainability do not simply reassure students; they can also increase pressure by making errors, responsibility, and the need to use artificial intelligence effectively more salient. The artificial neural network results corroborate these patterns. This study extends research on algorithm attitudes and offers guidance for creating more supportive artificial intelligence learning environments. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

24 pages, 3687 KB

Open AccessArticle

The Impact of Generative Artificial Intelligence Use on Perceived English Learning Achievement: The Roles of Use Behavior and Task–Technology Fit

by Zhongrui Wang and Shibao Guo

Behav. Sci. 2026, 16(5), 643; https://doi.org/10.3390/bs16050643 - 25 Apr 2026

Viewed by 524

Abstract

The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students’ perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance [...] Read more.

The rapid advancement of generative artificial intelligence (GAI) has intensified interest in its potential to support English learning in higher education. However, the mechanisms through which students’ perceptions and motivations translate into learning achievement remain unclear. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task–Technology Fit (TTF) theory, this study investigates how undergraduate students’ use of GAI relates to perceived English learning achievement and under what conditions these associations are amplified. Using covariance-based structural equation modeling (CB-SEM), data from 537 undergraduate students across five public universities in China were analyzed. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and artificial intelligence self-efficacy significantly predict GAI use. In turn, use behavior mediates their relationships with perceived English learning achievement. Task–Technology Fit further moderates the link between use behavior and learning achievement, with stronger associations observed when GAI functionalities are perceived as closely aligned with task requirements. These results highlight the importance of use behavior and task alignment in explaining how GAI is associated with students’ perceived English learning achievement and extend technology acceptance research within AI-supported language learning contexts. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

Other

Jump to: Research

27 pages, 1609 KB

Open AccessSystematic Review

Effectiveness of AI-Supported Game-Based Learning: A Systematic Review of Outcomes, Challenges, and Future Directions

by İsmail Kaşarcı and Eyüp Yurt

Behav. Sci. 2026, 16(7), 1050; https://doi.org/10.3390/bs16071050 - 24 Jun 2026

Viewed by 307

Abstract

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and [...] Read more.

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

(This article belongs to the Special Issue AI Use and Academic Development)

► Show Figures

Figure 1

Journal Menu

Journal Browser

AI Use and Academic Development

Share This Special Issue

Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI