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

Ramteja Sajja; Yusuf Sermet; Muhammed Cikmaz; David Cwiertny; Ibrahim Demir

doi:10.3390/info15100596

journal article Open Access Sep 30, 2024

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

Ramteja Sajja

Yusuf Sermet

Muhammed Cikmaz David Cwiertny Ibrahim Demir

Information Vol. 15 No. 10 pp. 596 · MDPI AG

View at Publisher Save 10.3390/info15100596

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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References

46

[1]

Altbach, P.G., Reisberg, L., and Rumbley, L.E. (2010). Trends in Global Higher Education: Tracking an Academic Revolution, Brill. 10.1163/9789004406155

[2]

Means, B., Toyama, Y., Murphy, R.F., Bakia, M., and Jones, K. (2023, August 18). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies, US Department of Education, May 2009, Available online: http://files.eric.ed.gov/fulltext/ED505824.pdf.

[3]

Holmes, W., Bialik, M., and Fadel, C. (2023, August 18). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Available online: http://oro.open.ac.uk/60255/. 10.58863/20.500.12424/4276068

[4]

Popenici, S., and Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn., 12. 10.1186/s41039-017-0062-8

[5]

Sermet "A Comprehensive review of ontologies in Hydrology towards guiding next-generation artificial intelligence applications" J. Environ. Inform. (2023)

[6]

Sermet "An intelligent system on knowledge generation and communication about flooding" Environ. Model. Softw. (2018) 10.1016/j.envsoft.2018.06.003

[7]

A Semantic Web Framework for Automated Smart Assistants: A Case Study for Public Health

Yusuf Sermet, Ibrahim Demir

Big Data and Cognitive Computing 10.3390/bdcc5040057

[8]

Zhang "Using artificial intelligence to improve pain assessment and pain management: A scoping review" J. Am. Med. Inform. Assoc. (2022) 10.1093/jamia/ocac231

[9]

Bandyopadhyay "An embedding based IR model for disaster situations" Inf. Syst. Front. (2018) 10.1007/s10796-018-9847-6

[10]

Winkler "Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis" Proc. Acad. Manag. (2018) 10.5465/ambpp.2018.15903abstract

[11]

Fryer "Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners" Comput. Hum. Behav. (2017) 10.1016/j.chb.2017.05.045

[12]

Gautam, A., Sit, M., and Demir, İ. (2022). Realistic river image synthesis using deep generative adversarial networks. Front. Water, 4. 10.3389/frwa.2022.784441

[13]

Demiray, B.Z., Sit, M., and Demir, İ. (2021). DEM Super-Resolution with EfficientNetV2. arXiv. 10.1007/s42979-020-00442-2

[14]

Li "U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding" Sci. Total Environ. (2023) 10.1016/j.scitotenv.2023.161757

[15]

Ewing "Interactive hydrological modelling and simulation on client-side web systems: An educational case study" J. Hydroinformatics (2022) 10.2166/hydro.2022.061

[16]

Sit "Web-based data analytics framework for well forecasting and groundwater quality" Sci. Total Environ. (2021) 10.1016/j.scitotenv.2020.144121

[17]

Ramirez "HydroLang: An open-source web-based programming framework for hydrological sciences" Environ. Model. Softw. (2022) 10.1016/j.envsoft.2022.105525

[18]

Ramirez "HydroLang Markup Language: Community-driven web components for hydrological analyses" J. Hydroinformatics (2023) 10.2166/hydro.2023.149

[19]

A Review on Artificial Intelligence in Education

Jiahui Huang, Salmiza Saleh

Academic Journal of Interdisciplinary Studies 2021 10.36941/ajis-2021-0077

[20]

Essel "The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education" Int. J. Educ. Technol. High. Educ. (2022) 10.1186/s41239-022-00362-6

[21]

Crompton "The potential of artificial intelligence in higher education" Rev. Virtual Univ. Católica Del Norte (2021)

[22]

Liu "Investigation of users’ knowledge change process in learning-related search tasks" Proc. Assoc. Inf. Sci. Technol. (2019) 10.1002/pra2.63

[23]

Greenhow "Artificial intelligence in education: Addressing ethical challenges in K-12 settings" AI Ethics (2021)

[24]

Ewing "An ethical decision-making framework with serious gaming: A smart water case study on flooding" J. Hydroinformatics (2021) 10.2166/hydro.2021.097

[25]

Bahja, M. (2021). Natural Language Processing Applications in business. E-Business—Higher Education and Intelligence Applications, IntechOpen. 10.5772/intechopen.92203

[26]

Neumann, M., Rauschenberger, M., and Schön, E. (2023, January 16). ‘We Need To Talk About ChatGPT’: The Future of AI and Higher Education. Proceedings of the 2023 IEEE/ACM 5th International Workshop on Software Engineering Education for the Next Generation (SEENG), Melbourne, Australia. 10.1109/seeng59157.2023.00010

[27]

Pursnani "Performance of ChatGPT on the US fundamentals of engineering exam: Comprehensive assessment of proficiency and potential implications for professional environmental engineering practice" Comput. Educ. Artif. Intell. (2023) 10.1016/j.caeai.2023.100183

[28]

Sajja "Platform-independent and curriculum-oriented intelligent assistant for higher education" Int. J. Educ. Technol. High. Educ. (2023) 10.1186/s41239-023-00412-7

[29]

Tack, A., and Piech, C. (2022). The AI Teacher Test: Measuring the Pedagogical ability of Blender and GPT-3 in educational dialogues. arXiv.

[30]

Lee "The rise of ChatGPT: Exploring its potential in medical education" Anat. Sci. Educ. (2023) 10.1002/ase.2270

[31]

Perkins "Academic integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond" J. Univ. Teach. Learn. Pract. (2023)

[32]

Audras "Virtual teaching assistants: A survey of a novel teaching technology" Int. J. Chin. Educ. (2022) 10.1177/2212585x221121674

[33]

ChatGPT for good? On opportunities and challenges of large language models for education

Enkelejda Kasneci, Kathrin Sessler, Stefan Küchemann et al.

Learning and Individual Differences 2023 10.1016/j.lindif.2023.102274

[34]

Brown "Language Models are Few-Shot Learners" Neural Inf. Process. Syst. (2020)

[35]

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language Models Are Unsupervised Multitask Learners, OpenAI.

[36]

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.

[37]

Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.

[38]

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., and Le, Q. (2019). XLNET: Generalized Autoregressive Pretraining for Language Understanding. arXiv.

[39]

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.

[40]

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.

[41]

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.

[42]

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., and Ray, A. (2022). Training language models to follow instructions with human feedback. arXiv.

[43]

Greene, R., Sanders, T., Weng, L., and Neelakantan, A. (2023, August 18). New and Improved Embedding Model, December 15, 2022. Available online: https://openai.com/blog/new-and-improved-embedding-model.

[44]

Gunawan "The Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents" J. Phys. Conf. Ser. (2018) 10.1088/1742-6596/978/1/012120

[45]

Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust speech recognition via Large-Scale Weak Supervision. arXiv.

[46]

Brockman, G., Eleti, A., Georges, E., Jang, J., Kilpatrick, L., Lim, R., Miller, L., and Pokrass, M. (2023, August 18). Introducing ChatGPT and Whisper APIs, March 01, 2023. Available online: https://openai.com/blog/introducing-chatgpt-and-whisper-apis.

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Metrics

375

Citations

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References

Details

Published: Sep 30, 2024
Vol/Issue: 15(10)
Pages: 596
License: View

Authors

R

Ramteja Sajja

Department of Electrical Computer Engineering, University of Iowa, Iowa City, IA 52246, USA; IIHR Hydroscience and Engineering, University of Iowa, 300 S. Riverside Dr., Iowa City, IA 52246, USA

Y

Yusuf Sermet

IIHR Hydroscience and Engineering, University of Iowa, 300 S. Riverside Dr., Iowa City, IA 52246, USA

M

Muhammed Cikmaz

IIHR Hydroscience and Engineering, University of Iowa, 300 S. Riverside Dr., Iowa City, IA 52246, USA

D

David Cwiertny

IIHR Hydroscience and Engineering, University of Iowa, 300 S. Riverside Dr., Iowa City, IA 52246, USA; Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52246, USA; Department of Chemistry, University of Iowa, Iowa City, IA 52246, USA; Center for Health Effects of Environmental Contamination, University of Iowa, Iowa City, IA 52246, USA

I

Ibrahim Demir

Department of Electrical Computer Engineering, University of Iowa, Iowa City, IA 52246, USA; IIHR Hydroscience and Engineering, University of Iowa, 300 S. Riverside Dr., Iowa City, IA 52246, USA; Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA 52246, USA

Funding

National Science Foundation (NSF) Award: NA22NWS4320003

National Oceanic I& Atmospheric Administration (NOAA) Award: NA22NWS4320003

Cite This Article

Ramteja Sajja, Yusuf Sermet, Muhammed Cikmaz, et al. (2024). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. Information, 15(10), 596. https://doi.org/10.3390/info15100596

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

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