Academic

Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling

arXiv:2604.05345v1 Announce Type: new Abstract: 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 eva

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Aisvarya Adeseye, Jouni Isoaho, Seppo Virtanen, Mohammad Tahir · · 1 min read · 3 views
#cs.AI

arXiv:2604.05345v1 Announce Type: new Abstract: 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.

Executive Summary

The article introduces a novel Dynamic Agentic AI Expert Profiler System Architecture designed to classify user expertise across four levels (Novice, Basic, Advanced, Expert) in multidomain interactions. Using a modular layered architecture built on LLaMA v3.1 (8B), the system evaluates natural language responses in real-time, achieving 83% to 97% accuracy in matching participant self-assessments. The study validates the system through static and dynamic phases, with the latter involving 402 live interviews where expertise is assessed incrementally. While the system demonstrates high reliability, discrepancies arise from self-rating biases, unclear responses, and LLM interpretation challenges. This work addresses critical gaps in context-aware AI-human interaction and has implications for adaptive learning, personalized AI assistants, and professional assessment tools.

Key Points

  • ▸ Proposes a modular, agentic AI profiler system for real-time expertise classification in multidomain interactions.
  • ▸ Achieves 83%-97% accuracy in matching participant self-assessments across static and dynamic evaluation phases.
  • ▸ Uses LLaMA v3.1 (8B) as the foundational model for text preprocessing, scoring, aggregation, and classification.
  • ▸ Dynamic phase assesses expertise incrementally during live interviews, enhancing adaptability compared to static methods.
  • ▸ Identifies key challenges: self-rating bias, unclear responses, and LLM misinterpretation of nuanced expertise.

Merits

Innovative Modular Architecture

The system's layered architecture, built on LLaMA v3.1 (8B), allows for scalable and adaptive expertise profiling across diverse domains, addressing a critical need in context-aware AI-human interaction.

High Accuracy in Expertise Classification

With 83%-97% accuracy in matching participant self-assessments, the system demonstrates robust performance, validating its reliability for real-world applications.

Dynamic Real-Time Assessment

The dynamic phase, involving 402 live interviews, showcases the system's ability to evaluate expertise incrementally, a significant advancement over static post-hoc analyses.

Multidomain Applicability

The system's architecture is designed to generalize across domains, making it versatile for applications in education, professional training, and customer service.

Demerits

Limited Generalizability of LLaMA v3.1 (8B)

The reliance on a specific model version (LLaMA v3.1 8B) may constrain broader applicability, as performance could vary with different model architectures or sizes.

Self-Rating Bias and Ambiguity in Responses

Discrepancies between profiler predictions and participant self-assessments often stem from self-rating biases or unclear responses, highlighting inherent challenges in subjective evaluation.

Nuanced Expertise Interpretation Challenges

Occasional misinterpretations by the LLM, particularly in cases of nuanced expertise, suggest limitations in capturing complex or domain-specific knowledge structures.

Ethical and Bias Concerns in Profiling

The system's use of expertise profiling raises potential ethical concerns, including the risk of reinforcing stereotypes or discriminatory practices if not carefully monitored and validated.

Expert Commentary

This study represents a significant advancement in the development of context-aware AI systems, particularly in its real-time, dynamic approach to expertise profiling. The modular architecture built on LLaMA v3.1 (8B) demonstrates scalability and adaptability, though its reliance on a specific model version may limit broader applicability. The high accuracy rates (83%-97%) are commendable, but the identified discrepancies—rooted in self-rating biases, ambiguous responses, and LLM interpretation challenges—highlight areas for further refinement. Ethically, the system's potential to reinforce biases or discriminatory practices cannot be overstated, necessitating robust validation frameworks and transparency in deployment. From a practical standpoint, the implications for adaptive learning and personalized AI are profound, but policy makers must prioritize ethical guidelines to ensure fair and equitable use. Future work should explore cross-cultural validation and integration with psychometric methods to enhance the system's robustness and generalizability.

Recommendations

  • ✓ Expand the system's validation to include cross-cultural and multilingual contexts to ensure generalizability across diverse user groups.
  • ✓ Incorporate psychometric validation techniques to improve the reliability and objectivity of expertise classifications.
  • ✓ Develop bias mitigation strategies, such as adversarial training or fairness-aware algorithms, to address potential ethical concerns in profiling.
  • ✓ Collaborate with domain experts to refine the system's ability to interpret nuanced expertise, particularly in specialized fields like medicine or law.
  • ✓ Establish ethical guidelines and regulatory frameworks for AI profiling, including transparency requirements and user consent protocols.
  • ✓ Explore alternative models and architectures to assess the system's performance beyond LLaMA v3.1 (8B), ensuring flexibility and scalability.
  • ✓ Integrate the profiler system with adaptive learning platforms to enable real-time, expertise-based content delivery in educational and corporate training settings.

Sources

Original: arXiv - cs.AI

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