How Technology Assists the Feedback Process in a Learning Environment: A Review
Abstract
The availability and utilization of feedback play a crucial role in the development of students' knowledge and skills. Technology-enhanced feedback is taking on an increasing role in education because of limited resources and large classes. These systems can assist the feedback process holistically by generating, delivering, and supporting the use of feedback. Four databases were searched to identify empirical studies on technology-enhanced feedback between 2015 and 2021. In the 61 studies screened, feedback systems were classified and analyzed according to their role in the feedback process, revealing the main techniques used, the characteristics of the feedback, and the impact on students. The results demonstrate the current state of research in technology-enhanced feedback, and future research can refer to this article for further exploration.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Research on student uptake of technology-based feedback and the characteristics of quality feedback in technology remains limited.
“However, in their concluding remarks both Munshi and Deneen (2018), Huang et al. (2023) point at important blind spots: Research on student uptake of technology-based feedback and the characteristics of quality feedback in technology remains limited.”
From Same assignment—two different feedback contexts: lower secondary students’ experiences with feedback during a three draft writing process (2025) · cites this paper as Huang et al., 2023
Students assigned to the AI feedback context received generated and transformed feedback information from a software technology they trusted.
“Students assigned to the AI feedback context received generated and transformed feedback information (Huang et al., 2023;Munshi and Deneen, 2018) from a software technology they trusted.”
From Same assignment—two different feedback contexts: lower secondary students’ experiences with feedback during a three draft writing process (2025) · cites this paper as Huang et al., 2023
Technology-enhanced feedback should be easy to act on.
“This means that technology enhanced feedback should be easy to act on Huang et al. (2023), Munshi and Deneen (2018).”
From Same assignment—two different feedback contexts: lower secondary students’ experiences with feedback during a three draft writing process (2025) · cites this paper as Huang et al. (2023)
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Penetrating the Fog: Analytics in Learning and Education.
Siemens, G., & Long, P. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1,752 · see citing works
What makes for effective feedback: staff and student perspectives
Dawson, P., Henderson, M., Mahoney, P., Phillips, M., Ryan, T., Boud, D., & Molloy, E. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1467877 · Cited by 514 · see citing works
An overview of learning analytics
Clow, D. (2013) · Teaching in Higher Education · doi:10.1080/13562517.2013.827653 · Cited by 436 · see citing works
Facilitating productive use of feedback in higher education
Jonsson, A. (2013) · Active Learning in Higher Education · doi:10.1177/1469787412467125 · Cited by 414 · see citing works
Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & Weerdt, J. D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104094 · Cited by 292 · see citing works
Some Effects of Feedback on Communication
LEAVITT, H. J., & Mueller, R. A. H. (1951) · Human Relations · doi:10.1177/001872675100400406 · Cited by 137 · see citing works
Enhancing Flipped Mathematics Education by Utilising GeoGebra
Weinhandl, R., Lavicza, Z., Hohenwarter, M., & Schallert-Vallaster, S. (2020) · International Journal of Education in Mathematics Science and Technology · doi:10.46328/ijemst.v8i1.832 · Cited by 80 · see citing works
McCoy, L., Pettit, R. K., Lewis, J. H., Allgood, J. A., Bay, R. C., & Schwartz, F. N. (2016) · BMC Medical Education · doi:10.1186/s12909-016-0530-7 · Cited by 58 · see citing works
Hsia, L., & Hwang, G. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12986 · Cited by 47 · see citing works
Mobile response system: a novel approach to interactive and hands-on activity in the classroom
Fuad, M., Deb, D., Etim, J. S., & Gloster, C. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9570-5 · Cited by 37 · see citing works
Improving summary writing through formative feedback in a technology‐enhanced learning environment
Kim, M. K., & McCarthy, K. S. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12516 · Cited by 33 · see citing works
Brown, G., & Harris, L. R. (2012) · Figshare · Cited by 26 · see citing works
Self-citationKaewunruen, S. (2019) · Education Sciences · doi:10.3390/educsci9020136 · Cited by 17 · see citing works
Lewis, S., Heath, G., Lim, L., & Roberts, R. (2021) · Student Success · doi:10.5204/ssj.1623 · Cited by 17 · see citing works
Munshi, C., & Deneen, C. C. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316832134.017 · Cited by 14 · see citing works
Coogle, C. G., Larson, A. L., Ottley, J. R., Root, A. K., & Bougher-Muckian, H. R. (2019) · Topics in Early Childhood Special Education · doi:10.1177/0271121419831414 · Cited by 13 · see citing works
Sato, T., Sandars, J. E., Brown, J., & Rogers, S. N. (2020) · BMJ Simulation & Technology Enhanced Learning · doi:10.1136/bmjstel-2020-000669 · Cited by 13 · see citing works
Dissecting assessment: A paradigm shift towards technology-enhanced assessments
Said, M. M. T., Aravind, V. R., Ferdinand, D., & Umachandran, K. (2019) · World Journal on Educational Technology Current Issues · doi:10.18844/wjet.v11i2.4256 · Cited by 11 · see citing works
Instructors’ Perspectives Of Giving Audio And Video Feedback: Can You Hear Me Now?
Sims, J. M. (2016) · UND Scholarly Commons (University of North Dakota) · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Technology-Enhanced Feedback System Usability in the Context of Self-Regulation Promotion
Žerovnik, A. (2024) · Education Sciences · doi:10.3390/educsci14090948 · Cited by 23 · see citing works
Moltudal, S. H., Gamlem, S. M., Segaran, M., & Engeness, I. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1509904 · Cited by 12 · see citing works
A Brief Review of Generative Artificial Intelligence in Higher Education
Xu, Q., & Wang, N. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-2566-9_8 · Cited by 1 · see citing works
Smart Problem-Solving Using AI Driven Resource Kit
Mohanraj, A., Nithyasarathi, M., Mohammed Safil, M., Ravena, S., & Narthika, M. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895204
Student Voices in Formative Assessment Feedback
(2025) · Frontiers research topics · doi:10.3389/978-2-8325-6938-2
Alebaikan, R., Almassaad, A. Z., & Alajlan, H. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-09585-8
- Authors
- Huang, W., Brown, G. T. L., & Stephens, J. M.
- Affiliations
- University of Auckland (NZ)
- Venue
- 2023 IEEE International Conference on Advanced Learning Technologies (ICALT)
- Publisher
- IEEE
- Published
- 2023-07
- Pages
- 326-328
- OpenAlex
- W4387163189
- Cited by
- 4
- References
- 20
- Citations by year
- 2025: 2 · 2024: 2
- FWCI
- 9.46
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Advanced Learning Technologies (ICALT) (publishedVersion)
- ResearchSpace (University of Auckland) (submittedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- feedback in learning, feedback systems, automated feedback, large classes, learning outcomes
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
