Generative AI: A Systematic Review of Related Interfaces and Interactions
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The retrieval-grounded generation approach aligns with recent research on mitigating hallucinations and maintaining factual consistency in educational applications of generative AI.
“This retrieval-grounded generation approach aligns with recent research on mitigating hallucinations and maintaining factual consistency in educational applications of generative AI [17,18].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [18]
Concerns related to accuracy, bias, and pedagogical appropriateness of AI-generated content remain active areas of research and debate.
“Nevertheless, concerns related to accuracy, bias, and pedagogical appropriateness of AI-generated content remain active areas of research and debate [17,18].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [18]
Cites
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 3× · see citing works
“…[7], [3], [24], [11], [100], [68], [31], [55], [25], [43], [74], [50], [13], [69], [38], [92], [44], [29], [58], [4], [61], [108], [39], [70], [93], [19], [21], [56] ,[98] Conversing [57], [12], [73], [14], [86], [60], [67], [45], [2], [105] Collaborating [99], [106], [49], [79], [54], [40],…”
Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023) · Education Sciences · doi:10.3390/educsci13090856 · Cited by 877 · cited 3× · see citing works
“Lastly, prompt engineering and user mental model challenges were surfaced by studies like [68] and [51], illustrating how users struggle with prompt formulation and system expectations.”
Preiksaitis, C., & Rose, C. (2023) · JMIR Medical Education · doi:10.2196/48785 · Cited by 468 · see citing works
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
Chan, C. K. Y., & Tsi, L. H. Y. (2024) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2024.101395 · Cited by 387 · see citing works
Combining human and artificial intelligence for enhanced AI literacy in higher education
Tzirides, A. O. (., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y.-L., Santos, T. A. D., Searsmith, D., O'Brien, C., et al. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100184 · Cited by 268 · cited 8× · see citing works
“…[4], [61], [108], [39], [70], [93], [19], [21], [56] ,[98] Conversing [57], [12], [73], [14], [86], [60], [67], [45], [2], [105] Collaborating [99], [106], [49], [79], [54], [40], [33], [80], [107] Manipulating [23], [103], [30] Observing [101], [65], [20] Adjusting [32], [9] Navigating [87]…”
InfluentialSuh, S., Chen, M., Min, B., Li, T. J.-J., & Xia, H. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642400 · Cited by 148 · see citing works
Sedkaoui, S., & Benaichouba, R. (2026) · European Journal of Innovation Management · doi:10.1108/ejim-02-2024-0129 · Cited by 142 · see citing works
Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., & Abdullah-Al-Jubair, M. (2024) · IEEE Access · doi:10.1109/access.2024.3461874 · Cited by 123 · cited 6× · see citing works
“UX Dimension [63], [27], [3], [32], [31], [69], [38], [54], [16], [29], [70], [30], [9], [72] Engagement [41], [11], [78], [93], [101] Usability [23], [90], [103], [92], [102], [14], [58], [37] Emotional Impact [99], [48], [6], [47], [62], [24], [57], [68], [79], [51], [44], [39], [34], [19], [98],…”
InfluentialCo-creating digital art with generative AI in K-9 education: Socio-material insights
Vartiainen, H., Tedre, M., & Jormanainen, I. (2023) · International Journal of Education Through Art · doi:10.1386/eta_00143_1 · Cited by 73 · see citing works
Learning to Prompt in the Classroom to Understand AI Limits: A Pilot Study
Theophilou, E., Koyutürk, C., Yavari, M., Bursic, S., Donabauer, G., Telari, A., Testa, A., Boiano, R., Hernandez-Leo, D., Ruskov, M., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-47546-7_33 · Cited by 58 · see citing works
Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners
Putjorn, T., & Putjorn, P. (2023) · 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE) · doi:10.1109/icitee59582.2023.10317680 · Cited by 37 · see citing works
Fostering children’s creativity through LLM-driven storytelling with a social robot
Elgarf, M., Salam, H., & Peters, C. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1457429 · Cited by 28 · see citing works
Generative AI in education: best practices for successful implementation
AlAli, R., Wardat, Y., Al-Saud, K., & Alhayek, K. A. (2024) · International Journal of Religion · doi:10.61707/pkwb8402 · Cited by 25 · see citing works
Ortega-Arranz, A., Topali, P., & Molenaar, I. (2025) · Proceedings of the 15th International Learning Analytics and Knowledge Conference · doi:10.1145/3706468.3706533 · Cited by 6 · see citing works
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AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs
Angeioplastis, A., Konstantakis, M., Aliprantis, J., Ordoumpozanis, K., Varsamis, D., & Tsimpiris, A. (2026) · Information · doi:10.3390/info17020216 · Cited by 3 · see citing works
“Nevertheless, concerns related to accuracy, bias, and pedagogical appropriateness of AI-generated content remain active areas of research and debate [17,18].”
Self-citation
- Authors
- Ordoumpozanis, K., Konstantakis, M., Zoi, S., & Caridakis, G.
- Affiliations
- University of the Aegean (GR)
- Venue
- Proceedings of the 3rd International Conference of the ACM Greek SIGCHI Chapter
- Publisher
- ACM
- Published
- 2025-09-23
- Pages
- 39-47
- OpenAlex
- W4414277248
- Cited by
- 12
- References
- 14
- Citations by year
- 2026: 12
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Conference of the ACM Greek SIGCHI Chapter (CHIGreece) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- generative AI, human-AI interaction, AI interfaces, chatbots
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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