Generative AI Frameworks for Digital Academic Advising and Intelligent Student Supporst Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P114Keywords:
Generative Artificial Intelligence, Digital Academic Advising, Intelligent Student Support Systems, Large Language Models, Educational Data Analytics, Explainable AI, Decision Support Systems, Higher Education TechnologyAbstract
The growing scale and complexity of higher education have exposed limitations in traditional academic advising models, which are often manual, reactive, and constrained by limited human resources. As institutions serve increasingly diverse student populations across physical and digital learning environments, there is a critical need for intelligent, scalable, and personalized advising solutions. This paper presents a comprehensive framework for Generative Artificial Intelligence (GenAI)–driven digital academic advising and intelligent student support systems that integrates large language models, predictive analytics, and institutional academic data. The proposed framework enables natural language interaction, personalized academic planning, early risk identification, and career-oriented guidance by leveraging student records, learning management data, and policy knowledge bases. Generative AI enhances advising by providing context-aware, explainable recommendations and supporting scenario-based academic decision-making. To ensure responsible adoption, the framework incorporates ethical AI principles, including transparency, bias mitigation, privacy preservation, and human-in-the-loop governance. A modular, cloud-native, and API-driven architecture is proposed to support scalability and seamless integration with existing Student Information Systems and Learning Management Systems. Experimental insights and related studies indicate that AI-enabled advising can significantly improve student engagement, advising efficiency, and retention outcomes while reducing advisor workload. Overall, the framework demonstrates how generative AI can transform academic advising into a proactive, intelligent, and continuously learning support ecosystem that aligns institutional goals with student success and long-term educational outcomes
References
[1] Young Jones, A. D., Burt, T. D., Dixon, S., & Hawthorne, M. J. (2013). Academic advising: Does it really impact student success? Quality Assurance in Education, 21(1), 7–19. https://doi.org/10.1108/09684881311293034
[2] Khosravi, H., Kitto, K., & Williams, J. J. (2019). RiPPLE: A crowdsourced adaptive platform for recommendation of learning activities. arXiv. https://arxiv.org/abs/1910.05522
[3] Ciolacu, M., Tehrani, A. F., Binder, L., & Svasta, P. M. (2018, October). Education 4.0-Artificial Intelligence assisted higher education: early recognition system with machine learning to support students' success. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging(SIITME) (pp. 23-30). IEEE.
[4] Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N. R., Visvizi, A., & Hassan, S. U. (2022). Leveraging AI and machine learning for national student survey: actionable insights from textual feedback to enhance quality of teaching and learning in UK’s higher education. Applied Sciences, 12(1), 514.
[5] Loucif, S., Gassoumi, L., & Negreiros, J. (2020). Considering students’ abilities in the academic advising process. Education Sciences, 10(9), 254.
[6] Okewu, E., & Daramola, O. (2017, October). Design of a learning analytics system for academic advising in Nigerian universities. In 2017 international conference on computing networking and informatics (ICCNI) (pp. 1-8). IEEE.
[7] Becker, P., Tebes, G., Peppino, D., & Olsina Santos, L. A. (2019). Applying an improving strategy that embeds functional and non-functional requirements concepts. Journal of Computer Science & Technology, 19.
[8] Kurtanović, Z., & Maalej, W. (2017, September). Automatically classifying functional and non-functional requirements using supervised machine learning. In 2017 IEEE 25th international requirements engineering conference (RE) (pp. 490-495). IEEE.
[9] Iatrellis, O., Kameas, A., & Fitsilis, P. (2017). Academic advising systems: A systematic literature review of empirical evidence. Education Sciences, 7(4), 90.
[10] Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020). Pre trained models for natural language processing: A survey. Science China Technological Sciences, 63(10), 1872–1897. https://doi.org/10.1007/s11431 020 1647 3
[11] Raj, N. S., & Renumol, V. G. (2021). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113–148. https://doi.org/10.1007/s40692 021 00199 4
[12] Xu, J., Xing, T., & Van Der Schaar, M. (2016). Personalized course sequence recommendations. IEEE Transactions on Signal Processing, 64(20), 5340-5352.
[13] Cohen, E. L. (1984). Assessment, advising and early warning: Strategies for improving retention. Journal of College Reading and Learning, 17(1), 43-56.
[14] Picone, M., Cirani, S., & Veltri, L. (2021). Blockchain security and privacy for the Internet of Things. Sensors, 21(3), 892. https://doi.org/10.3390/s21030892
[15] Zhang, F., & Guan, S. (2022). Evaluating the effectiveness of teaching experimental design in universities in the context of information technology. Scientific Programming, 2022(1), 9087653.
[16] Sikkel, K., Spil, T. A., & van de Weg, R. L. (1999). A real-world case study in information technology for undergraduate students. Journal of systems and software, 49(2-3), 117-123.
[17] Majjate, H., Bellarhmouch, Y., Jeghal, A., Yahyaouy, A., Tairi, H., & Zidani, K. A. (2023). AI-powered academic guidance and counseling system based on student profile and interests. Applied System Innovation, 7(1), 6.
[18] Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, Article 103862. https://doi.org/10.1016/j.compedu.2020.103862
[19] Duldulao, A. V., & Duldulao, J. R. R. Online Student Academic Performance Monitoring and Evaluation System of the Quirino State University.
[20] Jayaram, Y. (2023). Data Governance and Content Lifecycle Automation in the Cloud for Secure, Compliance-Oriented Data Operations. International Journal of AI, BigData, Computational and Management Studies, 4(3), 124–133. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P113
[21] Bhat, J., & Sundar, D. (2022). Building a Secure API-Driven Enterprise: A Blueprint for Modern Integrations in Higher Education. International Journal of Emerging Research in Engineering and Technology, 3(2), 123–134. https://doi.org/10.63282/3050-922X.IJERET-V3I2P113
[22] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123–135. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P113
[23] Jayaram, Y., & Sundar, D. (2023). AI-Powered Student Success Ecosystems: Integrating ECM, DXP, and Predictive Analytics. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 109–119. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P113
[24] Bhat, J. (2023). Strengthening ERP Security with AI-Driven Threat Detection and Zero-Trust Principles. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 154–163. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P116
[25] Jayaram, Y., & Bhat, J. (2022). Intelligent Forms Automation for Higher Ed: Streamlining Student Onboarding and Administrative Workflows. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 100–111. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P110
[26] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-Auditing Deep Learning Pipelines for Automated Compliance Validation with Explainability, Traceability, and Regulatory Assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133–142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P114
[27] Bhat, J. (2022). The Role of Intelligent Data Engineering in Enterprise Digital Transformation. International Journal of AI, BigData, Computational and Management Studies, 3(4), 106–114. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P111
[28] Jayaram, Y. (2023). Cloud-First Content Modernization: Migrating Legacy ECM to Secure, Scalable Cloud Platforms. International Journal of Emerging Research in Engineering and Technology, 4(3), 130–139. https://doi.org/10.63282/3050-922X.IJERET-V4I3P114
[29] Nangi, P. R., & Settipi, S. (2023). A Cloud-Native Serverless Architecture for Event-Driven, Low-Latency, and AI-Enabled Distributed Systems. International Journal of Emerging Research in Engineering and Technology, 4(4), 128–136. https://doi.org/10.63282/3050-922X.IJERET-V4I4P113
[30] Jayaram, Y., Sundar, D., & Bhat, J. (2022). AI-Driven Content Intelligence in Higher Education: Transforming Institutional Knowledge Management. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 132–142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P115
[31] Bhat, J., & Jayaram, Y. (2023). Predictive Analytics for Student Retention and Success Using AI/ML. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 121–131. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P114
[32] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104–113. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P111
[33] Jayaram, Y., & Sundar, D. (2022). Enhanced Predictive Decision Models for Academia and Operations through Advanced Analytical Methodologies. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 113–122. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P113
[34] Bhat, J. (2023). Automating Higher Education Administrative Processes with AI-Powered Workflows. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 147–157. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P116
[35] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2023). A Multi-Layered Zero-Trust Security Framework for Cloud-Native and Distributed Enterprise Systems Using AI-Driven Identity and Access Intelligence. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 144–153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P115
[36] Bhat, J., Sundar, D., & Jayaram, Y. (2022). Modernizing Legacy ERP Systems with AI and Machine Learning in the Public Sector. International Journal of Emerging Research in Engineering and Technology, 3(4), 104–114. https://doi.org/10.63282/3050-922X.IJERET-V3I4P112
[37] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127–135. https://doi.org/10.63282/3050-922X.IJERET-V3I3P113
[38] Reddy Nangi, P., & Reddy Nala Obannagari, C. K. (2023). Scalable End-to-End Encryption Management Using Quantum-Resistant Cryptographic Protocols for Cloud-Native Microservices Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 142–153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P116
