An Evaluation of an Adaptive Learning System Based on Multimodal Affect Recognition for Learners with Intellectual Disabilities
Open accessAbstract
Abstract Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.
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.
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?
AI and VR technologies assist in the integration of students with disabilities in educational processes through the customised and individual training they provide [51,52,55,58], covering educational gaps due to inflexibility of traditional materials.
“AI and VR technologies assist in the integration of students with disabilities in the educational processes, through the customised and individual training they are able to provide [51,52,55,58], covering the educational gaps that exist due to the inflexibility of the traditional materials.”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [58]
AI systems can be used for the identification of students' affective states related to academic performance [58].
“Even further, AI systems can be used for the identification of students' affective states related to academic performance [58].”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [58]
One important drawback of AI and VR technologies is their inability to understand and process every kind of information they receive, leading sometimes to misleading or confusing responses from the system [53,56,58,59].
“One important drawback of the use of AI and VR technologies in educational processes is their inability to understand and process every kind of information they are receiving, leading sometimes to misleading or confusing responses from the system [53,56,58,59].”
From Impact of Artificial Intelligence and Virtual Reality on Educational Inclusion: A Systematic Review of Technologies Supporting Students with Disabilities (2024) · cites this paper as [58]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228 · see citing works
Handbook of Positive Psychology
(2001) · doi:10.1093/oso/9780195135336.001.0001 · Cited by 9,149 · see citing works
Generalized linear mixed models: a practical guide for ecology and evolution
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.10.008 · Cited by 8,910 · cited 1× · see citing works
“Wald z tests were performed under the central limit theorem to test the individual significance of fixed effect coefficients (Bolker et al ., 2009).”
MethodBozdogan, H. (1987) · Psychometrika · doi:10.1007/bf02294361 · Cited by 4,468 · cited 1× · see citing works
“Model fitting and variable selection utilised the Akaike Information Criterion (AIC) (Bozdogan, 1987), which performs better than the Bayesian Information Criterion (BIC) in the case of noncomplex linear models with disparate observations within nests (Vrieze, 2012).”
MethodVrieze, S. I. (2012) · Psychological Methods · doi:10.1037/a0027127 · Cited by 1,948 · cited 1× · see citing works
“Model fitting and variable selection utilised the Akaike Information Criterion (AIC) (Bozdogan, 1987), which performs better than the Bayesian Information Criterion (BIC) in the case of noncomplex linear models with disparate observations within nests (Vrieze, 2012).”
MethodHox, J. J., Moerbeek, M., & van de Schoot, R. (2017) · Routledge eBooks · doi:10.4324/9781315650982 · Cited by 1,723 · see citing works
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., & Edwards, T. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.07.045 · Cited by 1,713 · see citing works
Intellectual disability : definition, classification, and systems of supports
Disabilities, D. (2010) · Medical Entomology and Zoology · Cited by 1,370 · see citing works
Dynamics of affective states during complex learning
D’Mello, S. K., & Graesser, A. C. (2011) · Learning and Instruction · doi:10.1016/j.learninstruc.2011.10.001 · Cited by 1,101 · cited 5× · see citing works
“…may have maintained the higher levels of engagement, the rapid responsiveness, for example in response to frustration, may have moved them out of this state too early for learners to employ the thought, reflection and problem solving necessary to increase achievement (D’Mello & Graesser, 2012b).”
BackgroundMethodResultInfluentialBaker, R. S. J. D., D'Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.12.003 · Cited by 936 · cited 1× · see citing works
“Given the role of affect in learning (Baker, D’Mello, Rodrigo & Graesser, 2010; Kort, Reilly, & Picard, 2001), one of the most innovative applications of AIEd is in this detection Abstract Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning…”
MethodKort, B. W., Reilly, R., & Picard, R. W. (2002) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2001.943850 · Cited by 751 · see citing works
Affect and learning: An exploratory look into the role of affect in learning with AutoTutor
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004) · Journal of Educational Media · doi:10.1080/1358165042000283101 · Cited by 630 · see citing works
D'Mello, S. K., & Graesser, A. (2012) · TIIS · doi:10.1145/2395123.2395128 · Cited by 461 · cited 2× · see citing works
“They proposed using more challenging materials and this is consistent with findings from other studies (D’Mello & Graesser, 2012a).”
MethodResultAdvanced, Analytic, Automated (AAA) Measurement of Engagement During Learning
D’Mello, S. K., Dieterle, E., & Duckworth, A. L. (2017) · Educational Psychologist · doi:10.1080/00461520.2017.1281747 · Cited by 378 · cited 1× · see citing works
“The number of modalities used in this study could be supplemented with additional information from physiological measures and conversational cues (D’Mello, Dieterle, & Duckworth, 2017).”
MethodInvestigating Ceiling Effects in Longitudinal Data Analysis
Wang, L., Zhang, Z. J., McArdle, J. J., & Salthouse, T. A. (2008) · Multivariate Behavioral Research · doi:10.1080/00273170802285941 · Cited by 344 · see citing works
D’Mello, S. K., & Graesser, A. (2010) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9074-4 · Cited by 323 · see citing works
Affective computing in education: A systematic review and future research
Yadegaridehkordi, E., Noor, N. F. B. M., Ayub, M. N. B., Affal, H., & Hussin, N. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103649 · Cited by 252 · cited 1× · see citing works
“Compared to controls, this type of assistance was associated with significant learning gains in struggling college students.”
BackgroundCukurova, M., Kent, C., & Luckin, R. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12829 · Cited by 198 · cited 1× · see citing works
“The use of multimodal data, for instance in affect recognition, leads to more accurate machine learning classification models to augment decision-making processes (Cukurova, Kent & Luckin, 2019).”
MethodTaub, D. A., McCord, J. A., & Ryndak, D. L. (2017) · The Journal of Special Education · doi:10.1177/0022466917696263 · Cited by 136 · see citing works
Porayska‐Pomsta, K., Alcorn, A. M., Avramides, K., Beale, S., Bernardini, S., Foster, M. E., Frauenberger, C., Good, J., Guldberg, K., Keay‐Bright, W., et al. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3271484 · Cited by 136 · see citing works
Orekhova, E. V., & Stroganova, T. A. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00034 · Cited by 119 · cited 1× · see citing works
“It has been hypothesised that as a result of difficulties regulating their arousal levels, people with autism show atypical attention, specifically attention reorienting difficulties not just with social stimuli (Orekhova & Stroganova, 2014).”
BackgroundAffective learning: Improving engagement and enhancing learning with affect-aware feedback
Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., & Rummel, N. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9188-z · Cited by 105 · cited 3× · see citing works
“The initial ground truth exercise was carried out on a limited database compared with other studies and there was no opportunity to collect teacher rated affective states with which to compare automatically detected states as performed by Grawemeyer et al .”
MethodResultInfluentialAslan, S., Alyüz, N., Tanriover, C., Mete, S. E., Okur, E., D’Mello, S. K., & Esme, A. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300534 · Cited by 98 · cited 1× · see citing works
“One way is to provide real time information on engagement to teachers so that they can implement “just-in time” personalised interventions (Aslan et al., 2019).”
BackgroundBasawapatna, A. R., Repenning, A., Koh, K. H., & Nickerson, H. (2013) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2493394.2493404 · Cited by 92 · cited 1× · see citing works
“The affective model used was based on the pedagogical framework for online tutoring outlined by Basawapatna, Repenning, Han Koh, and Nickerson (2013) and Taheri et al .”
MethodScheiter, K., Schubert, C., Schüler, A., Schmidt, H., Zimmermann, G., Wassermann, B., Krebs, M., & Eder, T. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.05.005 · Cited by 71 · cited 1× · see citing works
“Scheiter et al .”
Aist, G., Kort, B. W., Reilly, R., Mostow, J., & Picard, R. W. (2003) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1109/icmi.2002.1167044 · Cited by 70 · cited 1× · see citing works
“However, there was still no significant difference between achievement scores from intervention and control sessions for the group as a whole or for each subgroup.”
BackgroundPowell, R. (2003) · Mental Retardation · doi:10.1352/0047-6765(2003)041 · Cited by 48 · see citing works
fASSERT: A Fuzzy Assistive System for Children with Autism Using Internet of Things
Sumi, A. I., Zohora, M. F., Mahjabeen, M., Faria, T. J., Mahmud, M., & Kaiser, M. S. (2018) · BI · doi:10.1007/978-3-030-05587-5_38 · Cited by 47 · cited 1× · see citing works
“Other approaches to detecting affective state in students with autism have prioritised the use of accelerometer data (Sumi et al., 2018).”
BackgroundHail the impossible:p‐values, evidence, and likelihood
Johansson, T. (2010) · Scandinavian Journal of Psychology · doi:10.1111/j.1467-9450.2010.00852.x · Cited by 38 · cited 1× · see citing works
“However, resultant p values should be taken conser-vatively given the experimental and subjective study design (Johansson, 2011).”
BackgroundGeneralized Tobit models: diagnostics and application in econometrics
Barros, M., Galea, M., Leiva, V., & Santos‐Neto, M. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1268572 · Cited by 36 · see citing works
Genetics with Jean: the design, development and evaluation of an affective tutoring system
Thompson, N., & McGill, T. J. (2016) · Educational Technology Research and Development · doi:10.1007/s11423-016-9470-5 · Cited by 31 · cited 3× · see citing works
“This reinforces the conclusion of Thompson and McGill (2017) that the benefits of an affective tutoring system may be more apparent in the longer term, rather than after a short evaluation.”
BackgroundResultInfluentialDisability-aware adaptive and personalised learning for students with multiple disabilities
Nganji, J. T., & Brayshaw, M. (2017) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-08-2016-0027 · Cited by 24 · cited 1× · see citing works
“Approaches to designing learning environments to meet the needs of learners with multiple disabilities have been suggested (Nganji & Brayshaw, 2017), by analysing the needs of the learner and then, matching specific learning resources to individuals through ontological modelling and adaptive…”
BackgroundChickerur, S., & Joshi, K. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12325 · Cited by 19 · cited 1× · see citing works
“Self-annotation of recordings (eg, Chickerur & Joshi, 2015) was not appropriate in our study due to the ability of the participants.”
BackgroundPersonalized, Affect and Performance-driven Computer-based Learning
Athanasiadis, C., Hortal, E., Koutsoukos, D., Lens, C. Z., & Asteriadis, S. (2017) · International Conference on Computer Supported Education · doi:10.5220/0006331201320139 · Cited by 17 · cited 2× · see citing works
“Research has shown that engagement increases when activities are tailored to personal needs and emotional states (Athanasiadis, Hortal, Koutsoukos, Lens, & Asteriadis, 2017).”
BackgroundResultSelf-citationEnhancing learning and teaching with technology
Nerantzi, C., Buckley, C., & Spiers, A. (2017) · e-space (Manchester Metropolitan University) · Cited by 13 · see citing works
Multimodal fusion based on information gain for emotion recognition in the wild
Ghaleb, E., Popa, M. C., Hortal, E., & Asteriadis, S. (2017) · Intelligent Systems Conference (IntelliSys) · doi:10.1109/intellisys.2017.8324224 · Cited by 12 · cited 1× · see citing works
“Initially, a Genetic Algorithm solution was proposed to learn appropriate weights per modality and use case (Ghaleb, Popa, et al ., 2017b).”
MethodSelf-citationMULTI-MODALITIES IN CLASSROOM LEARNING ENVIRONMENTS
Boulton, H., Brown, D. J., Standen, P., Belmonte, M. K., Kwiatkowska, G., Hughes‐Roberts, T., & Taheri, M. (2018) · INTED proceedings · doi:10.21125/inted.2018.0264 · Cited by 11 · see citing works
Self-citationAdaptive Learning Based on Affect Sensing
Tsatsou, D., Pomazanskyi, A., Hortal, E., Evaggelos Spyrou, A., Leligou, H. C., Asteriadis, S., Vretos, N., & Daras, P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93846-2_89 · Cited by 9 · cited 1× · see citing works
“Learning graphs contribute to the adaption of the learning process by adapting goal weights, so that they reflect both the contribution of the goals to the overall learning objective, leading to a smooth transition in knowledge acquisition (Tsatsou et al., 2018).”
BackgroundSelf-citation
Flagged first, then by citations
Research works printed but not linked: 4
- Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of the interplay between emotions and learn- ing. In Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT '01) (pp. 43-46). Madison, WI: IEEE Computer Society.
- Luckasson, R., Borthwick-Duffy, S., Buntinx, W. H. E., Coulter, D. L., Craig, E. M. (P.), Reeve, A., … The AAMR AD HOC Committee on Terminology and Classification. (2002). Mental retardation: Definition, classifica- tion, and systems of supports (10th ed.), Washington, DC: American Association on Mental Retardation.
- Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow optimal experience and its role in devel- opment. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89-105). New York, NY: Oxford University Press.
- Taheri, M., Brown, D., Standen, P., Sherkat, N., Cosma, G., & Langensiepen, C. (2018, September 4-6). State diagram for affective learning in an educational platform. Proc. 12th ICDVRAT with ITAG. Nottingham, UK.
Cited by
Sorted by citations
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · see citing works
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181 · see citing works
AI-enabled adaptive learning systems: A systematic mapping of the literature
Kabudi, T., Pappas, I., & Olsen, D. H. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100017 · Cited by 734 · see citing works
Vistorte, A. O. R., Deroncele-Acosta, Á., Ayala, J. L. M., Barrasa, Á., López-Granero, C., & Martí-González, M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1387089 · Cited by 362 · see citing works
Chalkiadakis, A., Seremetaki, A., Kanellou, A., Kallishi, M., Morfopoulou, A., Moraitaki, M., & Mastrokoukou, S. (2024) · Education Sciences · doi:10.3390/educsci14111223 · Cited by 106 · see citing works
Yenduri, G., Kaluri, R., Rajput, D. S., Lakshmanna, K., Gadekallu, T. R., Mahmud, M., & Brown, D. J. (2023) · IEEE Access · doi:10.1109/access.2023.3289496 · Cited by 135 · see citing works
“They suggested that longer exposure is also needed to figure out what effect it has on learning [57].”
BackgroundSelf-citationMultimodal Interaction, Interfaces, and Communication: A Survey
Dritsas, E., Τρίγκα, Μ., Troussas, C., & Phivos Mylonas, B. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9010006 · Cited by 108 · see citing works
“Ensuring smooth, seamless transitions between different input types and enabling users to naturally switch between them reflects the ultimate goal of multimodal interaction systems: creating interactions that feel as intuitive and adaptable as human communication itself [26–28].”
Wu, T., Lee, H., Wang, W., Lin, C., & Huang, Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00422-5 · Cited by 107 · see citing works
“Encouragingly, the nurtured pro - active learning stance fostered by RASEDS finds echoes in studies that advocate for a tailored learning approach in reducing frustration and cultivating a positive learning ambiance (Amin et al., 2023; Standen et al., 2020).”
BackgroundFernández-Herrero, J. (2024) · Education Sciences · doi:10.3390/educsci14080839 · Cited by 97 · see citing works
“The efficiency of these features logically depends on the accuracy of the emotional detection and response, which enables these automated resources to act as intelligent tutors sensitive to affects [65,66,71].”
InfluentialMeasuring emotions in education using wearable devices: A systematic review
Ba, S., & Hu, X. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104797 · Cited by 73 · see citing works
The Integration of Artificial Intelligence in Inclusive Education: A Scoping Review
Pagliara, S. M., Bonavolontà, G., Pia, M., Falchi, S., Zurru, A. L., Fenu, G., & Mura, A. (2024) · Information · doi:10.3390/info15120774 · Cited by 90 · see citing works
“Other studies, such as Standen et al. [28] and Dziatkovskii [33], highlight the use of machine learning algorithms and blockchain technologies to personalize learning experiences.”
Artificial Intelligence‐Supported Student Engagement Research: Text Mining and Systematic Analysis
Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025) · European Journal of Education · doi:10.1111/ejed.70008 · Cited by 52 · see citing works
Advances in Wearable Sensors for Learning Analytics: Trends, Challenges, and Prospects
Hong, H., Dai, L., & Zheng, X. (2025) · Sensors · doi:10.3390/s25092714 · Cited by 51 · see citing works
“Facial recognition techniques identify emotional states, aiding in recognizing engagement and frustration levels [17,41,42].”
Qazi, S., Kadri, M. B., Naveed, M., Khawaja, B. A., Khan, S. Z., Alam, M. M., & Su’ud, M. M. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048893 · Cited by 50 · see citing works
Rice, M. F., & Dunn, S. D. (2023) · Computers in the Schools · doi:10.1080/07380569.2023.2244935 · Cited by 64 · see citing works
“Ten of the articles discussed using AI to support the developmental processes of students with disabilities (Delavarian, 2015; Standen et al., 2020; Yang, 2022).”
BackgroundLiu, H., & Fan, J. (2026) · Language learning & technology · doi:10.64152/10125/73684 · Cited by 47 · see citing works
“This aligns with Standen et al. (2020), who argued that technological cognitive advantages must translate into emotional benefits to foster engagement.”
Human Intelligence Analysis through Perception of AI in Teaching and Learning
Kshirsagar, P. R., Jagannadham, D. B. V., Alqahtani, H., Naveed, Q. N., Islam, S., Thangamani, M., & Dejene, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9160727 · Cited by 55 · see citing works
“There is not a set of questions for each idea in advance [19].”
BackgroundHussein, E., Hussein, M., & Al-Hendawi, M. (2025) · Social Sciences · doi:10.3390/socsci14050288 · Cited by 58 · see citing works
Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021
Liu, Z., Ren, Y., Kong, X., & Liu, S. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331211064780 · Cited by 38 · see citing works
Michalski, S. C., Gallomarino, N. C., Szpak, A., May, K. W., Lee, G., Ellison, C., & Loetscher, T. (2023) · Virtual Reality · doi:10.1007/s10055-023-00759-2 · Cited by 36 · see citing works
“…training to each participant, for example, by adjusting the difficulty of a task relative to success in training (i.e. adaptive training) or repeating more challenging content at increasing intervals (i.e. spaced repetition) (Gray 2017; Standen et al. 2020; Zahabi and Abdul Razak 2020).”
Evaluating the Potential of Artificial Intelligence in Islamic Religious Education
Abubakari, M. S., Shafik, W., & Hidayatullah, A. F. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2728-9.ch010 · Cited by 35 · see citing works
Zhang, L., Carter, R. A., Basham, J. D., & Yang, S. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12725 · Cited by 31 · see citing works
AI-Based Adaptive Learning: A Systematic Mapping of the Literature
Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023) · JUCS - Journal of Universal Computer Science · doi:10.3897/jucs.90528 · Cited by 46 · see citing works
Using AI to Improve Accessibility and Inclusivity in Higher Education for Students with Disabilities
Mitre, X., & Zeneli, M. (2024) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet61869.2024.10837607 · Cited by 27 · see citing works
Effects of an adaptive computer agent-based digital game on EFL students’ English learning outcomes
Hwang, G., & Zhang, D. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-024-10396-4 · Cited by 33 · see citing works
“Besides, many studies support that adaptive digital learning environments based on students’ affective and behavioral factors contribute to students’ motivation and engagement (El-Sabagh, 2021; Schrader & Grassinger, 2021; Standen et al., 2020).”
Zhou, J., Shen, L., & Chen, W. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1458551 · Cited by 29 · see citing works
AI in Education and Learning: Perspectives on the Education Ecosystem
Niemi, H. (2024) · Springer eBooks · doi:10.1007/978-3-031-61187-2_11 · Cited by 22 · see citing works
Hocine, N., & Sehaba, K. (2023) · Human-Computer Interaction · doi:10.1080/07370024.2023.2242364 · Cited by 19 · see citing works
“Some studies recommended the uses of various activities and making them more ”real”, increasing difficulty levels for long-term evaluation (Morales-Villaverde et al., 2016), and using several learning materials (Standen et al., 2020) to enhance students’ engagement.”
BackgroundMethodInfluentialAltaleb, H., Mouti, S., & Beegom, S. (2023) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa58279.2023.10308834 · Cited by 18 · see citing works
Farhah, N. S., Adnan, M., Alqarni, A. A., Uddin, M. I., & Aldhyani, T. H. H. (2025) · IEEE Access · doi:10.1109/access.2025.3560863 · Cited by 17 · see citing works
Research on Students’ Adaptive Learning System Based on Deep Learning Model
Yang, X., Zhou, Z., & Xiao, Y. (2021) · Scientific Programming · doi:10.1155/2021/6593438 · Cited by 16 · see citing works
“We propose to use multimodal sensor data and machine learning system, which can improve the learning speed and keep the learner in the optimal emotional state [4].”
MethodRetractedMa, Y., Wei, Y., Shi, Y., Li, X., Tian, Y., & Zhao, Z. (2022) · Sustainability · doi:10.3390/su15010198 · Cited by 15 · see citing works
Adaptive Learning in AI Agents for the Metaverse: The ALMAA Framework
Xia, Y., Shin, S.-Y., & Lee, H.-A. (2024) · Applied Sciences · doi:10.3390/app142311410 · Cited by 15 · see citing works
Yun, H. J., & Cho, J. (2021) · Journal of Computers in Education · doi:10.1007/s40692-021-00211-x · Cited by 17 · see citing works
Orji, F. A., & Vassileva, J. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1015660 · Cited by 14 · see citing works
“A number of studies conducted in the area of affective computing in education have recognized the role of affective states especially the learning affective states in facilitating learning activities (Wiggins et al., 2015; Jiménez et al., 2018; Standen et al., 2020).”
BackgroundStalmach, A., D’Elia, P., Sano, S. D., & Casale, G. (2024) · Open Education Studies · doi:10.1515/edu-2024-0023 · Cited by 26 · see citing works
Overviewing Biases in Generative AI-Powered Models in the Arabic Language
Abubakari, M. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2530-9.ch013 · Cited by 12 · see citing works
AI Acceptance Among Prospective Social Studies and Culture and Arts Education Students
Dumagay, A. H., Balasa, K. A., Kunting, A. F., & Cabangcala, R. B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92611-2_11 · Cited by 11 · see citing works
Zou, D., Gu, M. M., & Yu, G. (2025) · System · doi:10.1016/j.system.2025.103783 · Cited by 13 · see citing works
The role of machine learning in autism spectrum disorder assessment and management
Reilly, A., Walsh, N., O’Reilly, D. P., Smyth, M., Gorman, K. M., Ostadabbas, S., & Power, C. (2025) · Pediatric Research · doi:10.1038/s41390-025-04566-0 · Cited by 11 · see citing works
Clark, A. K., Hirt, A., Whitcomb, D., Thompson, W. J., Wine, M., & Karvonen, M. (2025) · Education Sciences · doi:10.3390/educsci15020233 · Cited by 11 · see citing works
Qu, M. (2025) · European Journal of Education · doi:10.1111/ejed.70116 · Cited by 8 · see citing works
A data and knowledge-jointly driven multimodal intelligent system for enterprise culture assessment
Cao, R. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.08.083 · Cited by 8 · see citing works
Adapting Teaching and Learning in Higher Education Using Explainable Student Agency Analytics
Heilala, V., Jääskelä, P., Saarela, M., & Kärkkäinen, T. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-0230-9.ch002 · Cited by 7 · see citing works
AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability
Bosch, N. (2021) · Journal of Educational Data Mining · doi:10.5281/zenodo.5275314 · Cited by 7 · see citing works
“In some cases, models are also integrated into educational software to provide enhanced functionality such as automatic adaptations, which requires additional steps (Pardos et al., 2019; Sen et al., 2018; Standen et al., 2020).”
MethodHaddick, S., Brown, D. J., Connor, B., Lewis, J. E., Bates, M. I., & Schofield, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05039-8_25 · Cited by 7 · see citing works
Self-citationChen, S., Peng, D., Barbareschi, G., Chen, D. D., & Sato, C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713984 · Cited by 6 · see citing works
“AI can provide personalized learning experiences that adapt to the specific needs, learning pace, and abilities of individuals, effectively promoting learning for people with IDs [50, 54, 97].”
AI-Enabled Learning Ecosystems
Hari, S. S., Rajashree, R. H., Dharani, J., & Santhiya, C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8292-9.ch008 · Cited by 6 · see citing works
Hughes‐Roberts, T., Cui, V., Mahmud, M., & Brown, D. J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05039-8_16 · Cited by 6 · see citing works
Self-citationArtificial intelligence innovations challenges and emerging trends in engineering education
Rahman, A., Khandakar, A., Ayari, M. A., Naji, K. K., Al-Ali, A. K., Sellami, A., & Alhazbi, S. (2026) · Discover Education · doi:10.1007/s44217-026-01137-1 · Cited by 11 · see citing works
Standen, P., Brown, D. J., Kwiatkowska, G., Belmonte, M. K., Trigo, M. J. G., Boulton, H., Burton, A. M., Hallewell, M. J., Shopland, N., Gonzalez, M. A. B., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77857-6_31 · Cited by 4 · see citing works
Self-citationArtificial Intelligence and Safe Digital Learning for Children with Special Needs
Ahmed, S. M., Saleh, M. M., & Ahmed, M. E. (2025) · International Journal of Instruction · doi:10.29333/iji.2025.18311a · Cited by 4 · see citing works
“…location, thereby simplifying the learning process and fostering the development of technological skills vital in contemporary society, which in turn bolsters their self-confidence and enhances their learning and communication abilities (Neeharika, 2023 ; Standen et al., 2020; Zhai et al., 2023) .”
Zhao, J., Li, X., & Liao, H. (2025) · Language Teaching Research · doi:10.1177/13621688251352261 · Cited by 7 · see citing works
AI Strategies for Inclusive Education Resources and Support
Al-Rashaida, M. A. H., Moustafa, A., Mohsen, W. A., Aishan, H., Abuwatfa, R., & Khan, A. G. Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0573-8.ch012 · Cited by 3 · see citing works
Toward Personalized Adaptive Learning Using Artificial Intelligence and Physiological Signals
Hwang, J., & Shin, C. (2026) · IEEE Access · doi:10.1109/access.2026.3658124 · Cited by 3 · see citing works
“For example, Standen et al. [31] developed a tutor for students with intellectual disabilities that adjusted content delivery based on affective states inferred from GSR and HR. Xiong et al. [32] proposed a cognitive load recognition method using EEG and electrocardiography (ECG) signals,…”
Han, F., Deng, M., Yan, T., & Wang, H. (2026) · Learning and Individual Differences · doi:10.1016/j.lindif.2026.102876 · Cited by 4 · see citing works
Artificial intelligence in classrooms
Yoo, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24001-0.00017-8 · Cited by 5 · see citing works
Nacheva, R. V. (2025) · Acta Educationis Generalis · doi:10.2478/atd-2025-0009 · Cited by 3 · see citing works
MEDEIROS, A. H., da Costa, P. R., Costa, B. K., & da Silva, L. F. C. (2024) · International Journal of Innovation · doi:10.5585/2024.27640 · Cited by 2 · see citing works
Wei, Q., & Xie, Y. (2025) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit67332.2025.11267892 · Cited by 2 · see citing works
“The flourishing of online learning changed not only the components but also the intakes of knowledge provision by providing flexibility and accessibility to the learners [1].”
The Effect of Artificial Intelligence Over the Educational System (School and University)
Omidi, S., & Omidi, A. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-0993-3.ch008 · Cited by 2 · see citing works
Computational Vision for Analyzing Student Emotions in E-Learning: A Preliminary Study
do Nascimento, M. D., Oliveira, F., de Oliveira, V. W. S., Machado, R. M., Bomfim, M., & Damasceno, A. R. P. (2023) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343375 · Cited by 1 · see citing works
“As an example of these solutions, we can mention MaTHiSiS [5]; A learning engagement recognition framework [6]; and a framework for detecting emotions from 3D models [7].”
BackgroundMethodAttendance and learning platforms in college course success
Chang, I. (2025) · Active Learning in Higher Education · doi:10.1177/14697874251347189 · Cited by 1 · see citing works
“Others found that learning outcome metrics from LP analytics can provide early detection of students at risk (Dziuban et al., 2016), and with intellectual disabilities (Standen et al., 2020).”
Navigating the Ethical Landscape of Emotion Recognition Technologies
Arora, K., & Singh, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3658-9.ch006 · Cited by 1 · see citing works
Gesture-enhanced adaptive learning platform for personalized AI-driven education
Parveen, K., Obidallah, W. J., Alghamdi, A. A., Alduraywish, Y. A., & Shafiq, M. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2542897 · Cited by 4 · see citing works
“There is also an ethical concern when using artificial intelligence in education (Standen et al., 2020).”
Youssef, Y., Schneider, J., & Schiffner, D. (2025) · International Conference on Education Technology and Computer · doi:10.1109/icetc66579.2025.11387254 · Cited by 1 · see citing works
“Standen et al. [31] developed an adaptive learning system for learners with intellectual disabilities that used multimodal affect recognition (e.g., gaze, posture, facial expressions) to detect states such as engagement, boredom, and frustration, and dynamically adjusted learning content to…”
InfluentialGupta, A. (2026) · AI & Society · doi:10.1007/s00146-026-03072-z · Cited by 1 · see citing works
“The discussion here moves beyond a narrow understanding of embedding GenAI in teaching and learning practices, such as adaptive learning systems, that the majority of current studies and institutional policies seem to focus on (Balay-odao et al. 2025; Chiu et al. 2023; Standen et al. 2020).”
InfluentialTurabee, Z. B., Brown, D. J., Mahmud, M., Oikonomou, A., Rahman, M. A., Burton, A. M., Shopland, N., Clarke, D., & Gray, F. (2026) · MethodsX · doi:10.1016/j.mex.2026.104073 · Cited by 1 · see citing works
Self-citationMazzucato, A., Caputo, V., Roig-Vila, R., Cuerda, R. S., Kic-Drgas, J., Woźniak, J., Calleja, C., Burlò, E. T., & Sundberg, C. (2020) · OAR@UM (University of Malta) · doi:10.5281/zenodo.14638379 · Cited by 1 · see citing works
Self-citationHarris, M. C., Brown, D. J., Vyas, P., Lewis, J. E., & Connor, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35992-7_42 · Cited by 1 · see citing works
Self-citationÁvila, F. L. C., Vélez, K. N. G., Guerrero, B. R. C., Agurto, L. G. B., Guaraca, A. M. S., & González, L. J. L. (2024) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v8i3.11536 · Cited by 1 · see citing works
A Reinforcement Learning Framework for Personalized Adaptive E-Learning
Dahan, A., Roth, N., Pelosi, A. D., & Reiner, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71530-3_10 · Cited by 1 · see citing works
The Role of EdTechs in the Commodification of Knowledge
Hara, S. (2025) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-82031-1_25 · Cited by 1 · see citing works
Assistive Technology Utilization
Lee, H., & Wherfel, Q. M. (2022) · Advances in higher education and professional development book series · doi:10.4018/978-1-7998-9494-0.ch013 · Cited by 1 · see citing works
AI for Learners’ Emotions — A Perspective Approach of Analysis During Online Assessments
Sharon, S. J. S., Chitra, R. M. S., & Santhiya, C. (2025) · doi:10.1002/9781394302666.ch12 · Cited by 1 · see citing works
Research and Design of English Auxiliary Learning System Based on Human-Computer Interaction
Wang, Y. (2022) · International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) · doi:10.1109/icatiece56365.2022.10047288
“The second dimension is emotional attitude, which mainly examines whether learners have the characteristics of self orientation, active learning and peer assistance[8].”
Backgroundمکارى, ن. م. ج. م., & عجوة, م. س. (2023) · مجلة البحث العلمى فى التربية · doi:10.21608/jsre.2023.199369.1545
الشريف, د. م. م. ع. د. أ. ج. ط. (2023) · Mağallaẗ Al-Tarbiyyaẗ wa Ṯaqāfiẗ Al-Ṯifl · doi:10.21608/jkfb.2023.351554
Liu, X. (2024) · Journal of Education Humanities and Social Sciences · doi:10.54097/59qcyx80
Suman, D., Pleština, V., & Purković, D. (2024) · Politehnika · doi:10.36978/cte.8.2.1
Inteligencia Artificial en la educación y el empleo en el sector turístico:
Tapia, G. E. (2025) · ROTUR Revista de Ocio y Turismo · doi:10.17979/rotur.2025.19.1.11583 · Cited by 3 · see citing works
Improving learning material repositories using student profiles
Bravo, N. F. S., Martins, A. F., Rodrigues, T. B. D. S. F., Machado, M. D. O. C., Bernardino, H. S., Vieira, A. B., Barbosa, H. J. C., & de Souza, J. F. (2025) · Soft Computing · doi:10.1007/s00500-025-10450-0
“They defend that personalized learning maximizes student performance and, consequently, increases their satisfaction, consequently bringing impacts on education equity and accessibility (Serhan and Wel-come 2023; Standen et al. 2021, 2020).”
Alhawiti, A. O. (2025) · International Journal of Research in Special Education · doi:10.22271/27103862.2025.v5.i1c.117
Artificial Intelligence in Special Education
Donne, V. J., & Hansen, M. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2397-8.ch009
The Role of AI in Lifelong Learning
Shelton, K., & Dockens, A. L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2397-8.ch008
Generative AI and the Future of Personalised Islamic Religious Learning
Abubakari, M. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2302-2.ch003 · Cited by 1 · see citing works
Unveiling the Connection Between Online Adaptive Learning and Learning Outcomes in Higher Education
Kurniawan, C., Utami, D. D., Susilaningsih, S., Nordin, R. M., Surahman, E., Sumantri, A., Aniisah, A., & Hudha, M. N. (2025) · Tadris Jurnal Keguruan dan Ilmu Tarbiyah · doi:10.24042/tadris.v10i1.24659
Nguyen, T. Q., Nguyen, D. H., Tran, T. L., Nguyễn, T. H., Dang, T. T., & Hien, L. X. (2025) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse68178.2025.11309532
“For children with LDs, many studies have shown that ALS helps optimize foundational skills, such as adjusting text complexity for children with reading difficulties or gamifying basic arithmetic for children with math difficulties [8].”
Alhazmi, A. K. (2025) · Lecture notes in educational technology · doi:10.1007/978-981-95-0405-3_3
Balushi, A. M. A. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4982-4.ch009
Integration of AI tools in education: A TPACK analysis and pedagogical implications
Matović, S., & Babić, J. (2025) · doi:10.5937/int-themed-bg25083m
Jacob, T. K., & Mandal, L. (2026) · International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) · doi:10.1109/iementech202669403.2026.11434057
“In the middle and high school levels, this includes the applications of effort-based tutoring [9] and multi-modal recognition systems [16], [14].”
Ghosh, A., & Bhatia, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19678-1_31
From Function to Feeling: An Emotion-centered Framework for Inclusive Participatory Design
Wang, Y. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3787003
Jaleel, S., & George, M. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2026.150500155
A Systematic Literature Review on Artificial Intelligence Use in Personalized Learning
Drobnjak, A., Svetec, B., Divjak, B., & Botički, I. (2026) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2026.3711814
“Most personalization is based on study materials or other learning content, tailored to learners using variables such as knowledge level, learning style, and learning progress contents [13], [47], [55], [61], [84], [85], [86], [87].”
Lu, Y., Li, W., & Cai, Y. (2026) · Systems · doi:10.3390/systems14070846
Inteligencia artificial en educación: un estado de la cuestión en el contexto hispanoamericano
Sánchez, S. P., Marín, J. A. M., Torres, Á. F. R., & López-Belmonte, J. (2026) · Texto Livre Linguagem e Tecnologia · doi:10.1590/1983-3652.2026.63138
Wang, L. (2025) · Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII) · doi:10.1109/ceii67787.2025.00064
“Emotional feedback generation generates corresponding emotional feedback according to the emotional state of learners, such as encouragement, comfort or warning [3-4].”
- Authors
- Standen, P. J., Brown, D. J., Taheri, M., Galvez Trigo, M. J., Boulton, H., Burton, A., Hallewell, M. J., Lathe, J. G., Shopland, N., Blanco Gonzalez, M. A., Kwiatkowska, G. M., Milli, E., Cobello, S., Mazzucato, A., Traversi, M., & Hortal, E.
- Affiliations
- University of Nottingham (GB)
- Nottingham Trent University (GB)
- Corresponding author
- Standen, Penelope J.
- ORCID
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2020-07-29
- Volume
- 51
- Issue
- 5
- Pages
- 1748-1765
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W3045675805
- Cited by
- 106
- References
- 39
- Citations by year
- 2026: 19 · 2025: 37 · 2024: 19 · 2023: 14 · 2022: 10 · 2021: 6 · 2020: 1
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- British Journal of Educational Technology (publishedVersion, cc-by-nc)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc)
- Repository@Nottingham (University of Nottingham) (publishedVersion, cc-by-nc)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by-nc)
- UEL Research Repository (University of East London) (submittedVersion, cc-by-nc)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- European Commission Horizon 2020 (687772)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- adaptive learning systems, intellectual disability, affective states, student engagement, frustration, boredom, academic performance, content personalization, multimodal sensors, artificial intelligence in education
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
