Interest in data analytics is booming, and the need for business students to be proficient in this field remains strong

The gap

Evidence profile

Sourced from the recommendations and future work of the source papers, classified as general, spanning 2 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Artificial Intelligence–Powered Adaptive Learning Systems in Technical and Vocational Education: Effects on Skill Mastery, Self-Regulated Learning, and Learner Autonomy (2026) · Iconic Research and Engineering Journals · doi

  adaptive platforms: Prioritize learning systems that offer real-time diagnostics, personalized content sequencing, and feedback mechanisms to support individualized learning pathways for students. This aligns with broader evidence showing that adaptive systems can enhance learner engagement and outcomes when pedagogically sound and well-implemented. learning: Teachers should be empowered not just as content deliverers but as coaches and data interpreters who can guide students in interpreting their progress analytics and making productive learning decisions. This dual role enhances the benefit of adaptive systems. instructors as facilitators of b. Train c. Blend technology with hands-on practice: Adaptive systems should complement but not replace practical, hands-on TVET experiences. Tools like AI-powered platforms can prepare learners for workshop tasks and real-world practice, but physical performance evaluation remains essential. learning analytics b. Use blended data sources: Future studies should integrate trace data (e.g., interaction logs, time-on-task, error rates) with to deepen traditional performance measures understanding of feedback influences learning behaviour. adaptive how c. Explore hybrid AI models: Investigate how emerging techniques, such as generative AI and reinforcement improve adaptive instruction, especially for complex vocational tasks that require creative problem solving. learning, can further REFERENCES [1] Hariyanto, F. X. D., Kristianingsih, F. X. D., & Maharani, R. (2025). Artificial intelligence in adaptive education: a systematic review of techniques for personalized learning. Discover Education. [2] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. [3] Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420 IRE 1716175 ICONIC RESEARCH AND ENGINEERING JOURNALS 1350 © APR 2026 | IRE Journals | Volume 9 Issue 10 | ISSN: 2456-8880 DOI: https://doi.org/10.64388/IREV9I10-1716175 [4] Le Ying Tan, S., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial intelligence-enabled adaptive A review.Computers & Artificial Intelligence. platforms: learning [5] Little, D. (2015). Learning as dialogue: The dependence of learner autonomy on teacher autonomy. System, 23(2), 175–181. [6] Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing progress:

  generalrecommendations
  Keywords: learning adaptive systems artificial intelligence platforms education review real time personalized content feedback students learner
• Bridging the Gap: Teaching Data Analytics to Business Students using R (2026) · cited 1× · doi

  Interest in data analytics is booming, and the need for business students to be proficient in this field remains strong. Academic programs aimed at domain experts should offer data analytics courses that leverage students’ domain knowledge while accommodating their limited background in computer programming and statistics, thereby maintaining motivation through technically challenging yet accessible content. Drawing from the academic literature on the topic, student reflections, and our own experiences, we presented several lessons learned that may help other instructors develop similar courses. It is important to note that the course was developed in the context of the Dutch educational system, which is characterized by high industry involvement and oversight. The Dutch culture also values data-driven decision-making. Together, these factors ensured the needed legitimacy to invest resources in developing this course. This impetus and resources may not be available in other settings. However, we also acknowledge that the data science and data analytics fields are still developing rapidly, and therefore continuous monitoring and redesign of data analytics courses is necessary. For example, generative Artificial Intelligence (AI) tools (e.g. ChatGPT and Microsoft Copilot) are increasingly being used by students to help them code in R. Our initial experience with allowing students to use AI to generate code is that it can help students who have already mastered basic programming concepts make progress more quickly. However, when students lack a solid understanding of basic R syntax, they struggle to use prompts effectively and to apply AI-generated code to their assignments. Although the quality of AI-generated code is rapidly improving, hallucinations (references to non-existent packages or functions) remain a persistent issue. This can lead to frustration among both students and instructors, as they often Kokkinou, R. Mazinani, H. van Gils: Bridging the Gap: Teaching Data Analytics to Business Students using R 461 are unaware that hallucination is at play. Based on anecdotal evidence, we anticipate that as AI-generated code continues to improve, it will become an increasingly relevant educational tool. For now, however, further research is needed to explore how generative AI can best support student learning and to examine its associated ethical implications (Becker et al., 2023). Future course development should therefore critically assess the added value of these tools and consider how to integrate them effectively to enhance student learning outcomes.

  generalfuture work
  Keywords: students analytics code courses student help course generated business academic domain programming instructors dutch educational
• Harnessing Generative Artificial Intelligence in Computer Science Education: Pedagogical Innovation, Ethical Responsibility, and the Future of Assessment (2026) · Journal of University Teaching and Learning Practice · doi

  While the ethical and pedagogical challenges of GenAI integration are substantial, they exist alongside a growing body of evidence showcasing its transformative potential in reimagining assessment in computer science education. These emerging applications not only address 11 longstanding limitations in feedback delivery and scalability but also enable novel pedagogical approaches that promote critical thinking, creativity, and self-directed learning. One of the most promising developments is the use of GenAI as an intelligent tutor. Platforms such as ChatGPT, GitHub Copilot, and other code-focused large language models offer personalized support that adapts dynamically to individual learning needs. These tools provide real-time debugging assistance, suggest alternative problem-solving approaches, and deliver immediate feedback, capabilities that align with mastery learning models, where repeated practice and adaptive guidance foster deeper understanding (Leotta et al., 2024; Sun et al., 2024). By automating routine support tasks, GenAI can reduce instructor workload, allowing educators to focus on higher-order pedagogical functions such as mentorship, curriculum design, and ethical reflection. In parallel, the integration of GenAI with advanced learning analytics (LA) systems is opening new frontiers for data-driven education. AI tools such as OpenAI Codex and Whisper can process and analyse multimodal data sources, including code, audio transcripts, and collaborative dialogue. This enables more holistic insights into student learning behaviours, enabling timely interventions and tailored instructional strategies (Paiva et al., 2022; Pande & Mishra, 2023). Tools like VizChat, which provide contextualized visual analytics, are already enhancing educators' ability to interpret and act upon complex data sets (Yan et al., 2024). Furthermore, GenAI is facilitating the design of alternative assessment formats that promote authenticity and reduce opportunities for misconduct. Interactive oral assessments, scaffolded coding journals, and collaborative simulations can shift the focus from outputs to processes, emphasizing reflection, iteration, and explanatory depth (Angeli, 2022; Krautloher, 2024). These methods not only align more closely with real-world computing practices but also resist surface- level plagiarism by requiring learners to demonstrate conceptual understanding and articulate problem-solving rationale. Despite these advances, several critical issues demand ongoing attention. As AI systems become more pervasive, the temptation to over-automate must be resisted. Educators and institutions must ensure that AI tools do not marginalize human judgment or reduce complex pedagogical decisions to algorithmic outputs. This includes saf

  generalfuture work
  Keywords: genai learning pedagogical tools reduce educators ethical integration assessment education feedback approaches promote critical code

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