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Open research questions in Education and Learning Interventions

58 unresolved questions extracted from the limitations and future-work sections of 1,405 Education and Learning Interventions papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • This underscores that advanced AI features alone are insufficient; their success requires a supportive ecosystem of infrastructure and user support.

    The Impact of AI-Enhanced Digital Textbooks on University Students’ Academic Performance in China · 2026 · DOI

  • The results corroborate Alam, and Mohanty (2023)'s information-gap theory that states that “the curiosity is driven by the gap in knowledge that the learner experiences and the desire to fill the gap.

    Artificial Intelligence in Education and its Relationship with Learner Curiosity in Blended Learning Environments · 2026 · DOI

  • Suhier Abdelrahman Aburadeh Prof,Mohammad Saleem AlZboon Researcher/the university of Jordan The University of Jordan/School…

    The Degree to Which Public Jordanian University Students Possess Life Skills in Light of Metaverse Technology: Students’ Perspectives · 2026 · DOI

  • One limitation is the narrow demographic scope of the study sample, which consisted exclusively of senior pre-service teachers enrolled in a course specifically designed to address the educational uses of imVR. We collected data at a single point in time; thus, we did not account for the long-term evolution of behavioral intentions.

    Applying and Testing an Extensively Modified UTAUT-2 Model to Examine Pre-Service Teachers’ Intention to Use Immersive Virtual Reality in Their Teaching · 2026 · DOI

  • By demonstrating strong in-sample and out-of-sample predictive power, the proposed model offers a strong theoretical and practical framework for understanding pre-service teachers’ intentions to use imVR and for promoting imVR adoption in teacher preparation contexts. Hedonic motivation, habit, and performance expectancy impacted behavioral intention. Self-efficacy emerged as a central determinant, shaping participants’ perceptions of effort expectancy, performance expectancy, and hedonic motivation. Facilitating conditions significantly enhanced self-efficacy, effort expectancy, and habit. Age, sex, and prior experience showed limited or no impact. The structural model demonstrated strong in- and out-of-sample predictive/explanatory power, while the Importance-Performance Map Analysis identified habit and hedonic motivation as key areas requiring improvement. Education stakeholders should focus on building pre-service teachers’ confidence and competence in using imVR through structured, hands-on training and consistent access to well-supported technological environments. Efforts should prioritize cultivating habitual use of imVR by embedding it into regular teaching activities, lesson planning, and semester-long coursework, supported by readily available technical assistance. Finally, enhancing the enjoyment and engagement of imVR experiences can boost teachers’ intrinsic motivation, further strengthening their intention to integrate this technology into their future instructional practices. The study illustrates the need to integrate constructs such as self-efficacy, which are not fully addressed in traditional behavioral intention frameworks. Experimental designs that pay attention to sample selection are strongly advised to avoid random or invalid responses when evaluating users’ perceptions and intentions related to advanced or emerging technologies. Finally, the study demonstrates the necessity of suggesting and examining models that capture the complex and multifaceted dynamics of imVR adoption. 2 across diverse edu- Researchers should further validate the modified UTAUT cational contexts and participant demographics. Longitudinal and mixed methods designs are also advised. Expanding the model to include additional contextual variables can provide a more comprehensive understanding of barriers and facilitators influencing teachers’ intention to adopt imVR.

    Applying and Testing an Extensively Modified UTAUT-2 Model to Examine Pre-Service Teachers’ Intention to Use Immersive Virtual Reality in Their Teaching · 2026 · DOI

  • i. Adoption of AI-supported teaching strategies in Colleges of Education ii. Integration of digital literacy training into teacher education 39 IJLSER: E-ISSN 3027-1827 P-ISSN 3026-880X International Journal of Library Science & Education Research Published by Cambridge Research and Publications Vol. 12 No. 8 June, 2026. iii. Provision of technological infrastructure for effective learning iv. Further research on long-term impacts of AI in education REFERENCES Abdullahi, M., & Garba, S. (2023). Effects of hands-on learning strategies on students’ academic achievement in Biology. Journal of Science Education in Africa, 15(2), 45–58. Aina, J. K., & Adedoja, G. O. (2022). Innovative pedagogical approaches and students' academic achievement in science education. Journal of Science Education Research, 18(2), 45–58. Aljohani, N. R. (2020). Artificial intelligence in education: Current trends and future prospects. International Journal of Emerging Technologies in Learning, 15(10), 4–17. Bond, M., Buntins, K., Bedenlier, S., Zawacki‐Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(1), 1–30. https://doi.org/10.1186/s41239-019-0176-8 Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791 European Commission. (2022). The Digital Competence Framework for Citizens (DigComp 2.2). Publications Office of the European Union. Eze, C. U., & Okoli, J. N. (2021). Teaching methods and students’ academic performance in Biology in Nigerian Colleges of Education. Nigerian Journal of Educational Research, 19(1), 88–102. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410– 8415. https://doi.org/10.1073/pnas.1319030111 Hatlevik, O. E., Guðmundsdóttir, G. B., & Loi, M. (2015). Digital diversity among students: A multilevel analysis of digital competence. Computers & Education, 81, 345–353. https://doi.org/10.1016/j.compedu.2014.10.019 Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4 Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Holmes, W., & Tuomi, I. (2022).

    IMPACT OF HANDS-ON TEACHING STRATEGY ENHANCED WITH ARTIFICIAL INTELLIGENCE ON INTEREST AND PERFORMANCE OF BIOLOGY EDUCATION STUDENTS IN FEDERAL COLLEGES OF EDUCATION IN NORTH EASTERN, NIGERIA · 2026 · DOI

  • Mathematics teachers should integrate AI-assisted tools such as MathGPT and Wolfram Alpha as supplementary supports for Data and Probability lessons. These tools should be used for explanation, verification, practice, and reflection, but teachers should continue to provide direct instruction, conceptual clarification, monitoring, and assessment. Schools should provide orientation sessions on responsible AI use, including academic integrity, verification of generated responses, and the proper role of AI as a learning support. Administrators may also provide access support through reliable internet, shared devices, guided demonstrations, and classroom policies for ethical AI use. Curriculum implementers and mathematics coordinators may use the proposed enhanced AI-assisted mathematics instructional plan as a guide for teacher training and classroom implementation. Future researchers may conduct similar studies with longer intervention periods, larger samples, other mathematics topics, qualitative follow-up interviews, and comparisons of additional AI-supported platforms.

    Utilization of Ai-Assisted Instruction in Enhancing Mathematics Engagement and Achievement of Senior High School Students · 2026 · DOI

  • The rapid entry of generative artificial intelligence (GAI) into classrooms presents opportunities and constraints for teacher education, yet the processes through which preservice mathematics teachers (PMTs) develop integration competencies remain poorly understood.

    Examining the relationship between pre-service teachers’ technological pedagogical content knowledge and generative artificial intelligence acceptance · 2026 · DOI

  • Policy Recommendations Based on the research findings, educational policymakers and institutional leaders should prioritise the following strategic actions: Develop trust-centered AI integration policies that address data privacy, algorithmic transparency, and ethical AI governance, as trust was found to be a critical mediating construct in the AIEd adoption pathway. Invest in facilitating conditions, including AI-enabling infrastructure, faculty training programmes, and technical support services, given that FC demonstrated significant effects on both adoption intent and adoption success. Design AI adoption incentive schemes that emphasise both the pedagogical value (PE) and the enjoyment dimensions (HM) of AI use, while ensuring that cost-effectiveness (PV) is clearly communicated to faculty stakeholders. Embed self-efficacy development programmes for AI use within faculty professional development curricula, as SE showed a direct positive effect on both IA and SAU. 7.2 Recommendations for Future Research The following directions for future research are recommended: Revise and validate the IA construct: Future research should develop new items for measuring Intent to Adopt AI for Teaching with higher factor loadings (ideally ≥ 0.70), and should consider whether a bifactorial or formative specification better reflects the multidimensional nature of behavioral intention toward AI in educational contexts.

    Determinants fo AIEd Success: An Extended UTAUT2 Perspective with CB-SEM · 2026 · DOI

  • Private schools in Ho Chi Minh City, Vietnam, have pioneered STEAM; however, the drivers of effective implementation and their impacts on engagement, product quality, and evaluation remain underexplored.

    Factors Influencing STEAM Teaching at Private Schools in Vietnam: A Case Study of Ho Chi Minh City · 2025 · DOI

  • Since efficacy is domain and context specific, this study aimed to assess the level of efficacy and the sources of efficacy among the under-researched population, the boarding school ESL teachers, as a lot of research has focused on STEM subjects and daily school teachers.

    ESL Online Teaching: A Survey on Boarding School Teachers’ Efficacy and its Sources · 2025 · DOI

  • In conclusion, the Generative Artificial Intelligence Usage and Competence (GAIUC) Scale is expected to fill a gap in the literature by providing a validated tool to measure both the usage and competence of lifelong learners in using AI.

    Generative Artificial Intelligence as a Lifelong Learning Self Efficacy: Usage and Competence Scale · 2024 · DOI

  • The present review filled this gap in the literature by examining the degree to which conventional and IVR conditions have been controlled on instructional methods and content within the K-12 and higher education STEM literature base.

    Confounded or Controlled? A Systematic Review of Media Comparison Studies Involving Immersive Virtual Reality for STEM Education · 2024 · DOI

  • Future studies should examine a variety of teachers’ post-performance feedback practices in EAP or ESP (English for Specific Purposes) classrooms so that a knowledge base of pedagogically meaningful feedback practices is developed and made available for the language teachers today to rely on when they teach classes where the educational focus is not only the linguistic aspects of the target language, but the content or academic/professional competencies.

    フィードバックによる学術的社会化―EAP授業における教師のポスト・パフォーマンスフィードバックの会話分析 Socializing Students into Academics Through Feedback—Conversation Analysis of Teacher’s Post-performance Feedback to EAP Classroom Presentation · 2022 · DOI

  • ,auxiliary class,tutor),however,the effects of tutoring was being doubted by students;(3) students spent limited time on learning what they were really interested in and on autonomous reading,which meant that autonomy was insufficient in adolescents' extracurricular learning;(4)Scientific instruction on adolescents' extracurricular learning and on-line learning should be emphasized by educators.

    An Analysis of and Suggestions on Adolescents' Extracurricular Learning · 2010

  • Reviews of past research on psychosocial learning environments show that relatively few studies have involved the use of environment dimensions either as criterion variables in the evaluation computer education programs or with adult learners (in contrast to elementary and secondary school students).

    Using classroom psychosocial environment in the evaluation of adult computer application courses in Singapore · 2008 · DOI

  • This suggests that technology alone is insufficient; meaningful gains in reflection require intentional instructional design that guides learners through metacognitive cycles.

    Digital technologies and professional training: The case of human interaction specialists · 2026 · DOI

  • In its various forms, acceleration continues to be an evidence-based and widely used service provided to gifted students but remains controversial and unsupported in legislation in most states.

    Subject-Based Acceleration · 2022 · DOI

  • This multiple case study sought to examine preservice computer science teachers’ beliefs, motivational orientations, and teaching practices, as currently, they remain to be adequately researched.

    Preservice computer science teachers’ beliefs, motivational orientations, and teaching practices · 2022 · DOI

  • The use of avatars has provided integration of research evidence that increases intended behaviors; however, research is lacking on teacher self-efficacy change via an avatar experience.

    Using avatars to address teacher self-efficacy · 2020 · DOI

  • The study presented considers this gap in knowledge, analysing the effect of six different types of vicarious experience information on the self-efficacy of online workshop participants to complete a set task.

    The influence of general self-efficacy on the interpretation of vicarious experience information within online learning · 2019 · DOI

  • Within the vast experiential world of teachers, much research is lacking to progress from a photographic session of teacher behavior to a computerized tomography that reveals more information in this respect.

    Tiempo libre y calidad de vida desde el sí mismo docente · 2008

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58 open questions have been extracted from the limitations and future-work passages of 1,405 Education and Learning Interventions papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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