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Open research questions in Engineering Education and Technology

57 unresolved questions extracted from the limitations and future-work sections of 737 Engineering Education and Technology papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Viewed through the lens of CDP, our findings suggest that further research is required on the power dynamics at play when GenAI is implemented with a view to enhancing learner agency, and we encourage critical research that questions the assumption that technology will by its nature enhance learning and improve the agency of learners and teachers.

    Agency in the age of generative AI: a critical review of educational implications · 2026 · DOI

  • From a CDP perspective, this warrants additional attention – the product-focused nature of many AI-driven EdTech tools, and the drive for profit means that any benefits to learning are likely to be emphasized, while this possibility of disempowerment is likely to be downplayed on the commercial stage.

    Agency in the age of generative AI: a critical review of educational implications · 2026 · DOI

  • Limitation: This study is limited to the development of AI-based learning media without conducting classroom implementation or effectiveness testing.

    Language Learning Media Development Innovation: Maximizing the Use of Artificial Intelligence (AI) Technology · 2026 · DOI

  • Future research is needed to examine the practical implementation of Agentic AI in educational settings and to evaluate its impact on leadership practices, teacher development, and long-term educational sustainability.

    The Agentic AI bridge: a perspective on educational leadership, teacher development, and sustainability in the AI ERA · 2026 · DOI

  • As a conceptual contribution, the propositions advanced here require empirical testing before they can be treated as established findings. four lines of research are proposed, aligned with the framework’s structure. First, design-based research pilots that introduce Agentic AI supervisory workflows, measuring workload and decision-making effectiveness before and after (testing Mechanism 1).

    The Agentic AI bridge: a perspective on educational leadership, teacher development, and sustainability in the AI ERA · 2026 · DOI

  • Based on the study results we conclude that although the issue of AI has already been established within Communication Studies education, it remains insufficiently formalised as a part of the curricula.

    Artificial Intelligence in Communication Studies Education: Central European Perspectives · 2026 · DOI

  • Prioritise Teacher Training on AI and IoT Technologies Based on the findings from Table 2 (AI and Personalisation of Learning), it is evident that AI technologies can significantly enhance student engagement and achievement when implemented correctly. Teacher training in the use of AI-powered platforms is crucial for successful adoption. We recommend that education ministries implement comprehensive training programs to equip teachers with the necessary skills to effectively integrate AI into their teaching practices. This is aligned with Vygotsky’s social constructivism theory, which emphasises the importance of teacher scaffolding in creating interactive, student- centred learning environments. Invest in Affordable IoT Devices and Infrastructure Table 3 highlighted that cost and infrastructure are primary barriers to the implementation of IoT in classrooms. To address this, we recommend that educational policymakers collaborate with tech companies to create affordable, scalable solutions for schools, especially in under-resourced areas. This could involve subsidising IoT devices for classrooms and developing low-cost internet solutions. According to Piaget’s theory of cognitive development, providing interactive, real-time feedback through IoT tools supports active learning and helps students construct knowledge in an engaging manner. Develop Policies Addressing Data Privacy and Security Data privacy and security were identified as significant concerns in Table 3. To mitigate these risks, we recommend the creation of national guidelines for the ethical use of AI and IoT in education, with a particular focus on student data protection. These policies should align with ethical theories of privacy, ensuring that AI-driven systems used in schools comply with privacy standards such as GDPR (General Data Protection Regulation). 31 AI and IoT in Education Implement Pilot Programs for AI and IoT in Diverse Educational Settings Evidence from Tables 2 and 3 indicates that while AI and IoT show promise, their successful integration depends on the educational context. We recommend that pilot programs be launched in diverse educational environments to test AI and IoT applications in urban, rural, and underserved schools. This will allow policymakers to evaluate the effectiveness and scalability of these technologies before widespread adoption, ensuring that interventions are tailored to meet the needs of different student populations. Encourage Collaborative Research on AI-IoT Integration Building on the findings in Tables 1 and 2, we recommend fostering collaboration between academic researchers and technology developers to bridge the gap between AI and IoT research and real-world applications. This could be achieved by establishing research- practice partnerships that align with theories of technology acceptance (e.g., TAM model) and foster continuous improvements in AI-IoT integration in classrooms.

    AI and IoT in Education: Enhancing Classroom Interactivity and Student Engagement · 2026 · DOI

  • Future research should explore the scalability and sustainability of AI and IoT applications in low-resource settings to ensure these technologies benefit students from all backgrounds.

    AI and IoT in Education: Enhancing Classroom Interactivity and Student Engagement · 2026 · DOI

  • Future research on AI applications in education should consider broadening the coverage of the sample of students taking part in the investigation to include students in other courses and universities. In addition, longitudinal studies can also assess moving student behaviors and perceptions concerning AI technologies as they progress. Qualitative findings can be enriched through inference and correlation by including quantitative data on their impacts on students' learning outcomes, such as academic performance or AI usage Alave and Bearneza Research and Practice in Technology Enhanced Learning (2026) 21:47 Page 15 of 17 logs. Institutions must set workshops or modules to teach students how to use such tools while critically analyzing and using the outputs productively. Further, AI tool programmers may imbue their tools with explainable and multimodal features such as voice guidance, video-style tutorials, and graphic representations to increase congruence with education. Ultimately, teachers should create a mindset that reframes AI as not replacing human learning but as a co-pilot to support problem-solving, critical thinking, and independent learning through supportive co-agents in studies.

    Co-pilots in problem solving: A qualitative inquiry into AI-assisted learning in mathematics · 2026 · DOI

  • for Researchers Researchers need to examine the long-term impacts of these network structures on the resilience and cultural preservation of individual learners, as well as how this peer-driven learning ecology may enhance the workflows of advanced cul- turally grounded AI creation. Impact on Society When micro-entrepreneurs use Veo3 as an AI tool to preserve their culture and promote products relevant to their audience, this approach encourages individu- als’ computational empowerment.

    Cultivating Participatory Learning Ecologies: Social Network Analysis of Peer-Driven Learning Network · 2026 · DOI

  • for Practitioners through participation and collaboration in a peer-driven, participatory learning ecology rather than through a top-down pre-workshop learning ecology. The post-workshop peer-driven learning ecology encountered significant trans- formation in comparison to the pre-workshop learning ecology. The results support the following main hypothesis: (1) influence shifted from hierarchical figures (high betweenness) to active collaborators (high eigenvector centrality); (2) a core participatory sub-community emerged, while non-active participants were peripheral; and (3) this restructured network directly enabled sophisticated, iterative, and culturally grounded AI creation workflows among artisans. Learning designers must prioritize making “community share abilities” partici- patory design that requires peer interaction before central instruction. Practi- tioners should design collaborative tasks that generate practice-based network edges, connect learners directly to institutional resources, and monitor network health using centrality metrics to identify structural vulnerabilities.

    Cultivating Participatory Learning Ecologies: Social Network Analysis of Peer-Driven Learning Network · 2026 · DOI

  • Future research should focus on formu- lating standardized protocols for the transformation of qualitative data into precise network parame- ters to facilitate substantial cross-study comparisons and further SNA in the context of collaborative learning ecology.

    Cultivating Participatory Learning Ecologies: Social Network Analysis of Peer-Driven Learning Network · 2026 · DOI

  • In the future, researchers should (1) use longitudinal SNA to determine out how long peer-driven learning ecologies last and how they affect business re- sults and cultural preservation by examining how incremental individuals’ com- putational empowerment affects them, (2) create more detailed SNA edge defi- nitions to tell the difference between types of interaction (such as “help-seek- ing” and “co-creation”), and (3) set up standard procedures for turning qualita- tive data into network parameters so that studies can be compared.

    Cultivating Participatory Learning Ecologies: Social Network Analysis of Peer-Driven Learning Network · 2026 · DOI

  • Future research should investigate the scalability of the proposed framework across diverse educational contexts, disciplines, and institutions. Additionally, longitudinal studies are warranted to assess the sustained impact of deep learning technologies on student competencies, including career readiness and the acquisition of practical IT skills.

    Deep Learning Methods Towards a Pedagogical Framework and Implementation Strategy: A Study of Information Technology Education Curriculum Development in Indonesia · 2026 · DOI

  • Description - part of the future in education - balanced approach - great opportunity - balance with traditional methods - AI - should not replace learning - become more passive - balance - do not replace basic processes like writing and reading - Help Students - supervision - replace teachers - assistive tool - Technology is developing rapidly - ready to use AI - concrete training on how to use it - ChatGPT - other applications - training - school - use of AI - infrastructure - big obstacle - challenge - longer experience - virtual reality in learning - new ideas - expand knowledge - reduces time - implement automatic assessments - immediate feedback - automate the assessment process - personalized exercises - needs of each student - increases motivation - student engagement - opportunity for interactivity - attractive - Students can become passive - AI to do 'copy-paste.' - their basic skills like reading and writing, can be damaged - dependence on technology - create a balance - technology serves as an aid - critical thinking - AI can increase enthusiasm - internet - lack of training - concerns about privacy - have clear rules for protecting privacy - detailed protocols about the use of AI in schools - respect students' rights - be in line with ethical norms - children's emotional and social development - appropriate environment THE ROLE OF ARTIFICIAL INTELLIGENCE IN TEACHING: PERSPECTIVES FROM PRIMARY SCHOOL…

    The Role of Artificial Intelligence in Teaching: Perspectives from Primary School Teachers · 2026 · DOI

  • Our guidelines should be interpreted with respect to the context from which we collected data, that is, adult learning environments. While these tools represent a wide variety of functions, they may not capture the full spectrum of educational technologies in use across different institutions and contexts. Though we have demon- strated the practicality and applicability of the guidelines to produce meaningful change in AI technologies for adult learners, it is unclear if and how the guidelines can be applied beyond adult and online learning contexts. Some of the guidelines may generalize across different age groups and modalities, while others, such as scaffold- ing social competencies and supporting career-oriented goals, may be specific to adult learners balancing professional, personal, and educational responsibilities. In addition, our analysis relied on thematic coding and heuris- tic evaluation. Although we used reflexive thematic analysis and consensus-building sessions to strengthen reliability, the judgments necessarily reflect researcher and coder interpretation. While the guidelines capture stakeholder concerns and priorities, further em- pirical validation is needed to assess their impact on learning, en- gagement, and adoption in practice. Future work should extend these guidelines through empirical validation, examining how these guidelines relate to learning out- comes, motivation, and technology use in authentic instructional settings. Cross-institutional studies, such as workforce training, community colleges, or international contexts, could test whether the guidelines hold in settings with different learner demographics, institutional structures, and resource constraints. Such work would help determine which guidelines are broadly applicable and which are more context-specific.

    Guidelines for Designing AI Technologies to Support Adult Learning · 2026 · DOI

  • By building on an expanded notion of sociotechnical imaginaries, we show, through a review of science‐fiction narratives about technology and education, that the imaginaries underlying educational AI are lacking or limited.

    How to Imagine Educational <scp>AI</scp> : The Filling of a Pail or the Lighting of a Fire? · 2025 · DOI

  • Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.

    Algorithmic learning or learner autonomy? Rethinking AI’s role in digital education · 2025 · DOI

  • Addressing the underexplored epistemologies of AI literacy MOOCs and kindled by transformative learning in late modernity, this paper examines how the design of the MOOC Elements of AI prompts reflexivity over AI.

    Where is the reflexive ‘I’ in the Elements of AI? · 2024 · DOI

  • Looking ahead, AI has the potential to revolutionize education in several ways (5) : Personalized Learning: AI can cater to diverse learning styles and needs, providing step-by-step explanations for complex subjects. Enhanced Research: AI tools, like advanced summarizers, could simplify the process of finding and citing research sources. Tutoring Systems: AI can accommodate each student’s learning style by offering a more efficient and effective way to tutor them. Teacher Support: AI can assist in grading and administrative tasks, although it is unlikely to replace the nuanced role of teachers in fostering social skills and life lessons.

    How Artificial Intelligence Will Shape the Future of Education · 2024 · DOI

  • One year after the premiere of the DLN, NASA began adopting schools throughout the United States that were lacking the means to provide certain STEM programs typically due to financial and location limitations (NASA, 2013a).

    Beyond the Classroom, Shooting for the Stars · 2016 · DOI

  • A significant limitation is also the dominance of theoretical studies in the identified data. Given the theoretical nature of the majority of the papers identified, this is unlikely to be a significant limitation.

    Agency in the age of generative AI: a critical review of educational implications · 2026 · DOI

  • Future research should explore longitudinal transfer to real-world practice, domain- specific validation, and the interaction between readiness-oriented design and organizational culture.

    Transforming Learning with AI-Driven Avatars and Readiness in Education: From Traditional Pedagogy to Simulation-Based Mastery · 2026 · DOI

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57 open questions have been extracted from the limitations and future-work passages of 737 Engineering Education and Technology 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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