Artificial intelligence (AI) is rapidly transforming academic writing in higher education by enhancing teaching practices, writing assessment, and student learning. Despite the growing adoption of AI-powered tools such as ChatGPT, Grammarly, PaperPal, and Automated Writing Evaluation (AWE) systems, existing research remains fragmented across pedagogical, assessment, and teacher-related perspectives. This study systematically reviews the current evidence on AI in academic writing using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Guided by four research questions formulated through the PICo framework, a comprehensive search of Scopus and Web of Science identified 236 records, from which 24 peer-reviewed studies published between 2021 and 2026 met the inclusion criteria following screening, eligibility assessment, and quality appraisal. The findings reveal three dominant themes: (1) AI integration in academic writing teaching and learning, (2) AI-assisted assessment, feedback, and writing evaluation, and (3) teachers' perspectives, ethical issues, and AI adoption. Collectively, the reviewed studies demonstrate that AI enhances writing quality, learner engagement, self-regulated learning, and formative assessment through timely and personalized feedback. However, AI remains limited in evaluating higher-order writing competencies, including critical thinking, originality, and contextual reasoning, reinforcing the continued importance of human expertise in writing assessment. The review further highlights the influence of teachers' perceptions, ethical concerns, and institutional readiness on successful AI implementation. The study contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, Assessment for Learning, and Technology Acceptance Model frameworks. These findings provide practical implications for educators, institutions, and policymakers while identifying priorities for future research on responsible and pedagogically sound AI integration in academic writing within higher education.
Saidatul Akmar Zainal Abidin, Noor Hanim binti Rahmat· International journal of res...· 0 citations
The emergence of Artificial Intelligence (AI) tools like ChatGPT is changing the nature of academic writing by providing support in generating ideas, developing content, revision and editing. Although writing with AI offers benefits, little is known about its impact on learners’ writing strategies and motivation. Guided by Self-Determination Theory (SDT), this study examines the relationship between competence, autonomy, and relatedness in academic writing in the context of ChatGPT usage. This quantitative study involved 126 respondents from higher educational institutions. A 43-item Likert-scale questionnaire adapted from Raoofi et al. (2017) and Youssef et al. (2024) was used to collect data. The questionnaire was based on three constructs: autonomy, relatedness, and competence and showed high internal consistency (Cronbach’s α = .937). Data were analysed using descriptive, inferential, and correlation analyses in SPSS. Results show positive perceptions of using ChatGPT in improving academic achievement, critical thinking skills, and the motivation of students. Moreover, participants used metacognitive, cognitive, and effort-regulating writing strategies frequently. ChatGPT is a potential scaffold for supporting learners’ writing development, but it also motivates them by increasing their competence, autonomy, and relatedness.
Julina Munchar, T. A. Buhari, Sharifah Shahnaz Syed Husain et al.· International journal of res...· 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
Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GenAI), is rapidly reshaping higher education by transforming academic research, teaching practices, learning processes, and institutional approaches to technology adoption. However, the rapid expansion of AI also raises concerns regarding academic integrity, privacy, data ownership, algorithmic bias, misinformation, and responsible use. This systematic literature review (SLR) synthesises recent evidence on the role of AI in higher education, with particular attention to four dimensions: AI adoption, AI-assisted academic research, AI ethics, and AI-enabled teaching and learning. Following the PRISMA framework, studies were identified through Scopus and Web of Science and assessed using predefined inclusion, exclusion, and quality appraisal criteria. From the initial 260 records, the screening and eligibility processes resulted in 20 studies being included in the final qualitative synthesis. The findings reveal that AI-assisted academic research represents the most prominent research area (35%), followed by AI-enabled teaching and learning (25%), AI adoption (20%), and AI ethics (20%). The reviewed studies demonstrate that AI supports literature reviews, academic writing, text revision, data analysis, referencing, personalised learning, feedback, student engagement, and research productivity. At the same time, effective implementation requires AI literacy, critical judgement, ethical awareness, institutional guidance, and appropriate training. The review identifies a significant need for integrated and longitudinal research examining AI adoption, ethical practice, learning outcomes, critical thinking, research quality, and student engagement across diverse disciplines and contexts. The study concludes that AI should function as a supportive technology that complements human judgement rather than replacing academic responsibility, providing implications for universities, educators, researchers, students, and policymakers.
Noor Hanim binti Rahmat· International journal of res...· 0 citations
Growing use of generative AI technologies like ChatGPT has changed online learning and increased student motivation. This study explores online learning motivation and ChatGPT using Self-Determination Theory (SDT) to examine competence, autonomy, and relatedness in online learners. 189 academics from various fields participated in a quantitative survey. A five-point Likert scale-based 52-item questionnaire was derived from Ryan and Deci (2000), Fowler (2018), and Youssef et al. (2024). Competence, autonomy, and relatedness were not gender-specific across academic groupings. In the descriptive study, students rated the AI system's function in critical thinking, academic accomplishment, engagement, and learning motivation positively. The greatest competency item was students' practice of cross-checking ChatGPT knowledge with independent study (M = 4.06), whereas the most autonomous item was achieving good grades (M = 4.59). Relatedness was strong in social engagement and teacher support. They liked class discussions (M = 4.00) and found course materials meaningful (M = 4.28). Positive correlations were found between competence, autonomy (r =.550, p <.001), and competence and relatedness (r =.551, p <.001). The results support the Self-Determination Theory as a valid framework for online learning motivation and show that ChatGPT can promote learners' competence, autonomy, and relatedness if responsibly integrated into online learning settings. The work has major theoretical, pedagogical, and practical consequences for higher education AI-assisted learning.
E. S. Mohandas, Aini Faridah Azizul Hassan, Nor Azyyati Md Saad et al.· International journal of res...· 0 citations
Academic writing is perceived as a challenging task among ESL students as it involves cognitive, metacognitive, and self-regulatory skills. New norms of support in writing processes have been introduced with the emergence of artificial intelligence tools, such as ChatGPT. Therefore, grounded in Albert Bandura’s Social Cognitive Theory, this quantitative study explores how environmental (including these environmental AI shifts), personal cognitive, and behavioural factors influence the students’ academic writing processes. It aims to investigate if there are significant relationships among the three factors based on demographic variables, namely, genders, academic disciplines, and linguistic proficiencies. In addition, the study also examines ESL students’ perceptions of how these factors influence their writing processes. To achieve these, a survey was conducted among 165 ESL students from a Malaysian public university. The data, which was obtained through a 5-point Likert scale questionnaire, was analysed using SPSS. The analyses indicate a significant relationship between personal factors and gender, and different academic disciplines. On the other hand, linguistic proficiencies show a significant relationship across environmental and personal factors. Furthermore, students perceived environmental factors, particularly the use of ChatGPT, as a tool that encourages critical thinking and improves academic performance. The findings also revealed that the students relied on metacognitive and cognitive writing strategies and reflected their high resilience and effort regulation despite showing a lower consistency in regular writing practice. This study offers a foundation for more focused instructional interventions in higher education.
Nurul Syafieqah Jaafar, N. Rosly, Najwa Zulkifli et al.· International journal of res...· 0 citations