Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling

Computer Science > Artificial Intelligence [Submitted on 7 Apr 2026] Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen, Mohammad Tahir In today's artificial intelligence driven world, modern systems communicate with people from diverse backgrounds and skill levels. For human-machine interaction to be meaningful, systems must be aware of context and user expertise. This study proposes an agentic AI profiler that classifies natural language responses into four levels: Novice, Basic, Advanced, and Expert. The system uses a modular layered architecture built on LLaMA v3.1 (8B), with components for text preprocessing, scoring, aggregation, and classification. Evaluation was conducted in two phases: a static phase using pre-recorded transcripts from 82 participants, and a dynamic phase with 402 live interviews conducted by an agentic AI interviewer. In both phases, participant self-ratings were compared with profiler predictions. In the dynamic phase, expertise was assessed after each response rather than at the end of the interview. Across domains, 83% to 97% of profiler evaluations matched participant self-assessments. Remaining differences were due to self-rating bias, unclear responses, and occasional misinterpretation of nuanced expertise by the language model. Comments: Accepted to be Published in IEEE Conference on Artificial Intelligence (CAI) 2026 - May 8-10, 2026, Granada, Spain Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.05345 [cs.AI]   (or arXiv:2604.05345v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.05345 Focus to learn more Submission history From: Aisvarya Adeseye Mrs [view email] [v1] Tue, 7 Apr 2026 02:30:45 UTC (298 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)