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Cited by 850Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningEdcuational Technology Systems

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

Open access
Sajja, Ramteja, Sermet, Yusuf, Cikmaz, Muhammed, Cwiertny, David, Demir, Ibrahim·2024·Information
DOI 10.3390/info15100596
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Abstract

This paper presents a novel framework, artificial intelligence-enabled intelligent assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and natural language processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA’s capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled virtual teaching assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with learning management systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.

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Crossref · 10.3390/info15100596

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36 of 36 references verified0 citation errors · 0 not checkable

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Citations

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Show all 34 references

Research works printed but not linked: 8

  1. Tack, A., and Piech, C. (2022). The AI Teacher Test: Measuring the Pedagogical ability of Blender and GPT-3 in educational dialogues. arXiv.
  2. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., and Gehrmann, S. (2022). PaLM: Scaling Language Modeling with Pathways. arXiv.
  3. Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.
  4. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. (2019). XLNET: Generalized Autoregressive Pretraining for Language Understanding. arXiv.
  5. Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). ROBERTA: A robustly optimized BERT pretraining approach. arXiv.
  6. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2019). ALBERT: A lite BERT for self-supervised learning of language representations. arXiv.
  7. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P.J. (2019). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv.
  8. Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust speech recognition via Large-Scale Weak Supervision. arXiv.

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Metadata

Authors
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I.
Affiliations
  • University of Iowa (US)
  • Department of Electrical Computer Engineering, University of Iowa, Iowa City, IA 52246, USA
Corresponding author
Sajja, Ramteja
ORCID
Venue
Information
Publisher
MDPI AG
Published
2024-09-30
Volume
15
Issue
10
Pages
596
ISSN
2078-2489 (ISSN-L)
DOI
10.3390/info15100596
OpenAlex
W4402988539
Cited by
620
References
34
Citations by year
2026: 236 · 2025: 337 · 2024: 43
FWCI
231.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Information (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Oceanic I& Atmospheric Administration (NOAA) (NA22NWS4320003, 2230710)
  • National Science Foundation (NSF) (NA22NWS4320003, 2230710)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
Keywords
personalized learning, adaptive learning, higher education, virtual teaching assistants, natural language processing, cognitive load reduction, personalized learning paths, virtual learning environments, student engagement, student satisfaction

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