The issue also highlights several underexplored gaps

The gap

Evidence profile

Sourced from the limitations and abstract and future work and recommendations of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 5 journals. Those papers have been cited 57 times in total.

Supporting evidence — 5 representative gaps

• A Semester with Generative AI Tools: University Students Lived Experiences and Insights (2026) · TechTrends · doi

  Although this study offers valuable insights into college stu- dents’ experiences and perspectives after a full semester of exposure to generative AI tools, some limitations should be acknowledged. First, only five of the twelve enrolled stu- dents participated in the study, which may limit the repre- sentativeness of the findings and may not fully capture the experiences and perspectives of all students in the course. Furthermore, data saturation was not explicitly assessed; additional interviews with the remaining participants could have revealed new themes or perspectives. Future studies should consider employing purposive sampling strategies with larger samples to strengthen the transferability of find- ings. Second, the study was conducted within the context of an advanced media course, so the results specifically reflect students’ use of GenAI tools for media- and mass communication-related tasks. In other disciplines, where tasks and learning objectives differ, students might engage with GenAI tools differently. Therefore, caution should be exercised when generalizing these findings beyond the con- text of media and mass communication education. Appendix A: Interview Questions Protocol A. When presented with a wide range of AI tools, what varied ways do students use AI tools to conduct media production? 1. Tell me about how you learn an AI tool. 2. When attempting your assignments or projects how do you determine which AI tools to use? 3. What techniques did you discover when using these AI tools for media production? 4. How does knowing about these tools and their functions affect the way you prepare and approach media projects versus when you did not know about them? B. How did college students apply multiple AI tools in their professional practice? 1. How do you perceive the efficiency of AI tools in profes- sional settings, particularly in terms of saving time and being helpful even without extensive prior knowledge or skills? 2. How has the quality of your work been impacted by using AI tools? Do you feel that AI has improved the quality of your work? If so, in what ways? If not, why do you think that is? 3. How do AI tools influence your creativity in professional tasks? Do they generate new ideas or approaches you might not have considered without their help? 4. What does using AI tools in a professional context mean to you? How do you feel about incorporating AI into your professional work? C. What ethical perspectives do college students gain, eliminate or sustain in an AI-driven course over time? 1. Can you recall any moment you were concerned about ethical issues related to AI usage? What was it? How did you address it? 2. Before taking this course what were your thoughts on the ethical implications of AI tools on the media production industry or other fields? 3. Were there moments while doing your assignments/pro- jects or during class where you had to reconsider any of your existing ethical views on AI tools? 4. What ethical concerns do you think are most important when using AI tools in educational and professional set- tings? 5. How has your view on AI ethics changed since you started this AI course? Funding No funding was received to assist with the preparation of this manuscript. Data Availability The datasets generated during and/or analyzed dur- ing the current study are not publicly available as it was not permitted by the IRB.

  generallimitationsevidence 5/5
  Keywords: tools your media students course professional ethical perspectives using college tasks production dents experiences context
• The Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study (2024) · The Electronic Journal of e-Learning · cited 45× · doi

  However, the strong research methodology exhibited by this study may have some potential limitations, which need to be addressed by future research. First, sample size in the study can be a cause of not generalizing the findings. For this, the future scope of research may include larger and more diversified samples, which may allow a wider understanding of the spectrum with regard to the impact of AI integration within the educational environment, so as to mitigate this particular limitation. This may limit the generalization of results to other settings, given the exact educational context of this study. In future studies, the effects of AI integration could be studied in many other educational environments, wherein more general results may be brought into light. Secondly, the findings may be influenced by the specific AI tools used in this study, such as ChatGPT and Studiosity. While these tools offer valuable insights into AI's impact on student performance, motivation, and critical thinking, they may not fully represent the range of AI applications in education. This limitation suggests that further research is needed to explore the effects of different AI-driven tools in various educational contexts to better understand their broader implications. In addition, by virtue of self-report measures, the variables of motivation, engagement, and assessment of perspective may all be impacted by potential bias. This has, therefore, made the use of self-reported data in this study. For that reason, the following researches are recommended: they need to come up with the objective measures or observation data while still using the self-reports to enhance the reliability of the studies. These limitations can be addressed in future research programs to give a sturdy understanding of the implication of AI integration in the educational setup.

  generallimitationsevidence 5/5
  Keywords: educational future integration tools self potential limitations need addressed understanding impact limitation effects motivation measures
• A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward (2025) · Journal of Computer Assisted Learning · cited 11× · doi

  However, the issue also highlights several underexplored gaps, including the limited disciplinary adaptation of analytics tools, a lack of ongoing student involvement in feedback design, insufficient attention to ethical concerns and the physiological and motivational dimensions of assessment, and a limited understanding of the role of emerging technologies, in particular, Generative AI (GenAI).

  generalabstractevidence 5/5
  Keywords: limited issue highlights several underexplored gaps including disciplinary adaptation analytics tools lack ongoing student involvement
• The AI disruption in engineering education: an analysis of changing student norms through cultural historical activity theory (2026) · Journal of Computing in Higher Education · cited 1× · doi

  Future research could usefully explore the motivations and barriers among non-users providing a more balanced understanding of GenAI adoption. It would also be valu- able to examine how alignments or misalignments between implicit and formal rules The AI disruption in engineering education: an analysis of changing…1 3impact learning outcomes and the broader educational system. Taking a multi-stake- holder approach, including educators, program heads and administrators, may offer a more holistic view of GenAI integration. Additionally, longitudinal research could help track how student practices, institutional policies and educational norms evolve over time as GenAI become more embedded in engineering programs and other dis- ciplinary settings. In relation to the theoretical limitations discussed, future research could also explore ways to extend CHAT by integrating it with complementary frameworks such as TAM or UTAUT. Despite epistemological differences, such combinations may offer valuable insights into students’ tool selection and perceived usefulness, that is dimensions that CHAT does not explicitly account for. Moreover, our findings suggest a potential link between GenAI use and CHAT’s levels of human procedures: operations (automated actions), actions (goal-oriented problem-solving), and activi- ties (collective efforts toward complex objectives) (cf. Engeström et al., 1999). Fur- ther research could explore whether and how GenAI supports task automation at the operational level, individual goals at the action level, and complex problem-solving and collaboration at the activity level. It seems there is a connection between these levels and the extent to which tasks are automated from human to GenAI.

  generalfuture workevidence 5/5
  Keywords: genai explore chat level future engineering educational offer levels human automated actions problem solving complex
• Co-pilots in problem solving: A qualitative inquiry into AI-assisted learning in mathematics (2026) · Research and Practice in Technology Enhanced Learning · 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.

  generalrecommendationsevidence 5/5
  Keywords: students learning education tools future applications consider broadening coverage sample taking part investigation include courses

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