Jun 2026· FINGER : Jurnal Ilmiah Teknologi Pendidikan· Vol 5, pp. 303-315· 0 citations
TL;DR
The results collectively suggest is that enthusiasm alone cannot sustain instructional quality in the AI era; it is technical mastery in AI Competence that converts a teacher’s positive outlook into measurable performance gains.
Abstract
Background: The rapid spread of AI tools in education has moved the conversation from simply adopting technology to examining how it actually transforms what teachers do in the classroom. Yet most studies have tackled this question by looking at one variable at a time, leaving the structural pathways that connect readiness to real instructional outcomes largely unexplored. Aims: This study aims to investigate the structural relationships between teachers' technological readiness, specifically digital literacy, self-efficacy, and attitudes toward AI, and their actual instructional performance, while exploring the mediating role of AI competence. Methods: Survey responses from 220 teachers drawn from across the Indonesian archipelago were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4, proceeding through a two-stage measurement and structural assessment. Results: Teacher Digital Literacy (TDL), Attitude Towards Using AI Tools (ATT), and Self-Efficacy with Technology (SET) each proved to be significant predictors of TAC (p < 0.001). Of all the variables tested, TAC turned out to be the strongest direct predictor of TP (β = 0.540). A more striking finding was that ATT carried no meaningful direct influence on TP (β = 0.009, p = 0.870); instead, its entire effect flowed through TAC, a pattern that satisfies the conditions for full mediation. Multi-Group Analysis further confirmed that these relationships held regardless of how long a teacher had been in the classroom.Conclusion: What these results collectively suggest is that enthusiasm alone cannot sustain instructional quality in the AI era; it is technical mastery in AI Competence that converts a teacher’s positive outlook into measurable performance gains.
The rapid evolution of pedagogical paradigms in the 21st century has shifted focus toward digital integration, learner-centered frameworks, and hybrid instructional models. While technological infrastructure has advanced, educational success remains fundamentally dependent on the human element—specifically, teacher motivation. This study investigates the empirical relationship between teacher motivation dimensions (Intrinsic Motivation, Pedagogical Self-Efficacy, and Extrinsic Institutional Support) and learner satisfaction within 21st-century learning environments. Utilizing a cross-sectional survey design, paired data were collected from $N = 320$ secondary and higher-education educators alongside $N = 1,280$ corresponding students. Quantitative analyses were executed using IBM SPSS Statistics (v28.0) for preliminary exploratory data analysis and reliability testing, and IBM SPSS AMOS (v28.0) for Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The measurement model demonstrated strong construct validity and goodness-of-fit ($\chi^2/\text{df} = 2.14$, $\text{CFI} = 0.962$, $\text{RMSEA} = 0.048$). The structural path model revealed that Intrinsic Motivation ($\beta = 0.42, p < 0.001$) and Pedagogical Self-Efficacy ($\beta = 0.35, p < 0.001$) significantly and positively predict learner satisfaction, whereas Extrinsic Institutional Support exerts a strong indirect effect mediated through teacher self-efficacy. Implications for educational policy, administrative strategy, and professional development are discussed.
Bikram Das, Dr. Ananta Kr Jena· EPRA international journal o...· 0 citations
Feedback is a core mechanism of formative assessment, yet its effect on learning depends on how students conceive of and act upon it. This study tests an AI-enhanced student conceptions of feedback model in English as a Foreign Language (EFL) writing, relating six feedback conceptions to academic self-efficacy and self-regulation. Survey responses from 300 undergraduate students at a state university in Indonesia were analysed with partial least squares structural equation modelling (PLS-SEM) using 5,000 bootstrap subsamples. The measurement model met every reliability and validity criterion: outer loadings ranged from 0.743 to 0.888, composite reliability from 0.857 to 0.931 and average variance extracted from 0.609 to 0.750, while the Fornell-Larcker criterion, cross-loadings and HTMT ratios all confirmed discriminant validity. All seven structural paths were significant. Active use of AI feedback was the strongest antecedent of self-efficacy, followed by enjoyment, whereas ignoring feedback exerted a significant negative effect. Self-efficacy in turn strongly predicted self-regulation and accounted for 43.5% of its variance. The results indicate that what students do with feedback matters more than the source from which it originates, and that self-efficacy is the pivotal mechanism translating feedback experience into self-regulated learning.
Virgiawan Adi Kristianto, Sucipto, W. Sumbodo et al.· MATEC Web of Conferences· 0 citations
This research aims to investigate how generative AI literacy, self-efficacy, attitude, interest and dependence interact to influence academic work completion among university students in Ghana. It also seeks to identify the psychological pathways through which AI competence is associated with students' behaviour in the context of increasing generative AI adoption.
This research used a quantitative cross-sectional study to collect the data from 466 undergraduate students at KNUST. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the data among five constructs.
The results reveal a progressive behavioural pathway in which generative AI literacy positively predicts students' attitudes toward AI, which in turn strengthens their interest. This heightened interest significantly enhances students' self-efficacy in using AI, ultimately leading to dependence-induced task completion. Notably, self-efficacy emerged as the strongest predictor of task completion, underscoring both the empowering potential of AI use and the risk of increasing reliance on AI for academic work.
The cross-sectional design of this study limits its ability to interpret causality between the variables examined. Again, self-report measures are subject to common-method bias. Since the sample involved only KNUST undergraduate students, generalisation of the results cannot be assumed for other academic institutions. Finally, Partial Least Squares Structural Modelling assumes a linear relationship amongst all factors, thus may miss a nonlinear relationship that occurs within students' behaviours.
The findings provide actionable guidance for universities and policymakers seeking to advance SDG 4 (Quality Education) through responsible generative AI integration. Higher education institutions should embed structured AI literacy and ethical-use training within curricula to strengthen student self-efficacy while preventing unhealthy dependence. Assessment practices should be redesigned to emphasise critical thinking, creativity and reflective engagement rather than automated task completion. At the policy level, national higher education authorities are encouraged to develop AI-use guidelines that promote equity, academic integrity and learner autonomy. These practices support Emerald's impact agenda by translating empirical evidence into scalable educational interventions that enhance learning quality, student welfare and sustainable digital transformation in higher education.
The study underscores the need for institutional and national policies that guide responsible generative AI use in higher education while protecting student welfare. As AI literacy and self-efficacy increase, so does the risk of excessive dependence, with potential consequences for independent thinking, academic integrity and long-term cognitive development. Universities, particularly in developing contexts such as Ghana, must establish clear AI governance frameworks, embed ethical AI literacy into curricula and redesign assessments to prioritise critical engagement over automated outputs. Policy interventions should also ensure equitable access to AI training and support systems that promote student autonomy, well-being and sustainable learning practices in AI-mediated academic environments.
This study provides an understanding of GenAI use in African higher education by integrating AI literacy, self-efficacy, attitude, interest and dependence into one behavioural model that has been clearly tested in the Ghanaian context. The study provides new insights into how psychological factors and usage-based patterns shape students' dependence on Generative AI for class task completion, providing great insight for teachers who seek to continuously integrate AI into their work. The findings also support curriculum design and policy development by identifying the competencies and behavioural risks that must be addressed to guide effective AI use within universities.
Jacinta Mandy Baah, Eric Nana Ayeh Ntow, Jesse Ohene Boakye et al.· Journal of Applied Research...· 0 citations
With growing pressure in today’s world, students are facing burnout from meeting academic demands. This may influence students’ motivation and academic engagement, which causes growing concern in higher education. Therefore, this study is conducted to examine the influence of burnout on students’ motivation by integrating the Job Demands–Resources Model and Expectancy–Value Theory. These relationships are then tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). A questionnaire consisting of three components including value, expectancy, and affective was distributed to 125 undergraduate students. The results were then analysed using SmartPLS, following a two-stage procedure involving measurement and structural model assessment. The findings revealed that the measurement model exhibited satisfactory reliability and validity across all constructs. Structural model results revealed that disengagement exerted significant effects on all three motivational components (value, expectancy, and affective), indicating its strong and consistent influence on students’ motivational beliefs and emotional responses toward learning. Exhaustion, however, showed a more selective impact, significantly influencing expectancy but not value or affective components. Overall, the results suggest that motivational withdrawal affects students' motivation greater than emotional fatigue. These findings contribute to a more detailed understanding of how multiple aspects of burnout may affect students' motivation. The study also highlighted that PLS-SEM is useful in studying the complex relationship between psychological constructs. The results of this study contribute to addressing students’ loss of interest and highlight the role of educators in supporting student motivation during lessons.
Saidah Ismail, Haslinda Md. Isa, Noor Hanim binti Rahmat et al.· International journal of res...· 0 citations
Introduction This study explores the impact of AI literacy on high school EFL teachers' teaching anxiety, with a particular focus on the underlying psychological mechanisms. As AI technologies become increasingly embedded in educational contexts, teachers are expected to adapt to new digital tools. While AI literacy may offer practical benefits, its relationship with teaching anxiety through psychological and motivational pathways remains underexplored. This study aims to fill this gap by examining the serial mediation roles of teaching self-efficacy and work engagement in the relationship between AI literacy and teaching anxiety. Methods A quantitative research design was employed to examine the hypothesized relationships. Data were collected from a sample of 392 high school English teachers in China using 5-point Likert scales. The responses were analyzed using SmartPLS to conduct partial least squares structural equation modeling (PLS-SEM). The analysis included assessments of measurement model reliability and validity, followed by structural model evaluation. Mediation effects were tested through bootstrapping, and the Variance Accounted For (VAF) was calculated to assess the strength of indirect effects. While most serial mediation studies use SPSS with PROCESS, this study employed PLS-SEM, a relatively novel approach in educational research, to explore chain mediation. Results The results revealed that AI literacy negatively affected teaching anxiety, with higher AI literacy linked to lower anxiety. Both teaching self-efficacy and work engagement mediated this relationship, with self-efficacy serving as a stronger mediator. A significant serial mediation pathway was found: AI literacy was associated with higher self-efficacy, which in turn was related to increased work engagement and lower teaching anxiety. Discussions The results provide support for the proposed hypotheses and highlight the significant role of AI literacy in relation to psychological well being among EFL teachers. The findings suggest that AI literacy is associated with higher self-efficacy and engagement, which are in turn linked to lower teaching-related anxiety. These findings point to the importance of integrating AI literacy training alongside psychological and motivational support strategies in teacher development programs, particularly within the evolving landscape of AI-driven education.
In Australia, teachers are required to differentiate to meet the needs of students across the full range of abilities. However, research conducted in Australia has shown that the implementation of differentiated instruction (DI) in Australian classrooms is varied, and that teachers’ self-efficacy beliefs, attitudes, preparedness and contextual factors can influence teachers’ implementation. To date, these factors have typically been examined in isolation in Australia, limiting understanding of how they interrelate to shape DI enactment. Addressing this gap, this pilot study, novel in the Australian context, involved 87 primary and secondary school teachers to examine associations among teacher experience, attitude, self-efficacy, readiness and preparedness and DI practices. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings revealed that teacher efficacy (β = 0.290) and preparedness (β = 0.498) were associated with DI practices. Teacher attitudes were strongly associated with readiness (β = 0.621) and efficacy (β = 0.528), suggesting hypothesised associations through which attitudes relate to DI implementation, while teaching experience was positively associated only with teacher efficacy. Overall, the model explained 44% of the variance in DI practice, highlighting preliminary associations among psychological and contextual factors related to DI implementation. Implications are drawn for professional development and teacher education.
T. Porta, Gemma E. Scarparolo, Abu Nawas et al.· Social Psychology of Educati...· 0 citations