Agentic AI for Personalized Career Guidance: Concepts, Architecture, and Research Directions

DOI

10.2991/978-94-6463-978-0_50How to use a DOI?

Keywords

Agentic AI; Career Guidance; Personalization; Knowledge Graphs; Explainability; Ethics; Autonomous Systems; Recommender Systems; Human–AI Collaboration; Educational Data Mining; Labor Market Analytics; Career Path Recommendation; Reflective Learning

Abstract

This paper contains a detailed description of Agentic (artificial intelligence) AI) and its possible implementation in the personal career guidance system. The changes occurring in the labor markets along the digital spectrum necessitate the new, adaptive, context-sensitive development models that go beyond the stagnant recommender systems. The agentic AI that is typified by autonomy, planning, memory, and reflective learning can provide new career systems avenues that continuously adapt the individual desire to changing market environments. This review brings together recent conceptualizations and taxonomies of agentic AI and suggests our four-layer conceptual model, Perception, Cognition, Action, and Reflection. This framework recognizes how agentic capabilities can be used to facilitate dynamic user modeling, proactive guidance activities and re-integrating iterative feedback. Our raised points imply on explicability, fairness, and ethical decision-making in high stakes. We also find that there are several significant research issues, which bring to the fore the necessity of scalable integration, knowledge representation programs using knowledge graph, the development of accountability protocols and, validated evaluation using synthetic simulations. Lastly, we outline a research agenda that puts forward interdisciplinary methods to consider various parties involved in governance, and to establish agentic career guidance systems that are transparent, explainable and trustworthy.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rahul Vijaykumar Nalage
AU  - Reena Gunjan
PY  - 2025
DA  - 2025/12/31
TI  - Agentic AI for Personalized Career Guidance: Concepts, Architecture, and Research Directions
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 591
EP  - 598
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_50
DO  - 10.2991/978-94-6463-978-0_50
ID  - Nalage2025
ER  -