Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment
Abstract
Smart devices and intelligent technologies are enabling a smart learning environment to effectively promote the development of personalized learning and adaptive learning, in line with the trend of accelerating the integration of both. In this regard, we introduce a new teaching method enabled by a smart learning environment, which is a form of personalized adaptive learning. In order to clearly explain this approach, we have deeply analyzed its two pillars: personalized learning and adaptive learning. The core elements of personalized adaptive learning and its core concept are explored as well. The elements are four: individual characteristics, individual performance, personal development, and adaptive adjustment. And the core concept is referred to a technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitoring (enabled by smart technology) learners’ differences and changes in individual characteristics, individual performance, and personal development. On this basis, A framework of personalized adaptive learning is also constructed. Besides, we further explored a recommendation model of the personalized learning path. To be specific, personalized adaptive learning could be constructed from the following four aspects, namely, learner profiles, competency-based progression, personal learning, and flexible learning environments. Last, we explored a form of learning profiles model and a generative paths recommendation pattern of personal learning. This paper provides a clear understanding of personalized adaptive learning and serves as an endeavor to contribute to future studies and practices.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · see citing works
Kim, J., Lee, H., & Cho, Y. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10831-6 · Cited by 669 · see citing works
The role of ChatGPT in higher education: Benefits, challenges, and future research directions
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.29 · Cited by 561 · see citing works
A systematic literature review of personalized learning terms
Shemshack, A., & Spector, J. M. (2020) · Smart Learning Environments · doi:10.1186/s40561-020-00140-9 · Cited by 539 · see citing works
“For example, Chatti et al. (2010) and Peng, Ma and Spector (2019) are the ones who put the two most common terms used for personalized learning together and started to use “ personalized adaptive learning, ” which might be a good lead for future focus on what components are being used for each…”
BackgroundSelf-citationAI-based personalized E-learning systems: Issues, challenges, and solutions
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022) · IEEE Access · doi:10.1109/access.2022.3193938 · Cited by 386 · see citing works
Leading teachers' perspective on teacher-AI collaboration in education
Kim, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12109-5 · Cited by 376 · see citing works
Artificial intelligence-enabled adaptive learning platforms: A review
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100429 · Cited by 207 · see citing works
Mapping out a research agenda for generative artificial intelligence in tertiary education
Lodge, J. M., Thompson, K., & Corrin, L. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8695 · Cited by 209 · see citing works
Contrino, M. F., Reyes-Millán, M., Vázquez-Villegas, P., & Membrillo-Hernández, J. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00292-y · Cited by 158 · see citing works
“Personalized learning refers to an educational strategy where the objectives, the sequence of the content, the learning pace, and the instruction can vary according to the student’s needs (Peng et al., 2019).”
BackgroundArtificial Intelligence in Health Professions Education
Shankar, P. R. (2022) · Archives of Medicine and Health Sciences · doi:10.4103/amhs.amhs_234_22 · Cited by 40 · see citing works
Exploring Students’ Use of a Mobile Application to Support Their Self-Regulated Learning Processes
Baars, M., Khare, S., & Ridderstap, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.793002 · Cited by 36 · see citing works
Leveraging Existing Trained AI Models for Enhanced Interview Preparation
Gupta, S. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.7.52 · Cited by 5 · see citing works
Ko, E. G., & Hughes, J. E. (2026) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2026.100560 · Cited by 3 · see citing works
Lauren, P. (2024) · 2024 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893139 · Cited by 2 · see citing works
- Authors
- Peng, H., Ma, S., & Spector, J. M.
- Venue
- Smart Learning Environments
- Published
- 2019-09-18
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-7091
- Cited by
- 346
- References
- 0
- Language
- en
- Funders
- the State Scholarship Fund of China Scholarship Council
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