mlsanityData to 7 Oct 2026

AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching

arXiv 2305.17619 ↗First posted 28 May 2023cs.CL cs.AI cs.LGcited by 8

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural Language Processing (NLP) techniques, it would be possible to quickly determine which calls are most relevant for coaching purposes. In this paper, we present AI Coach Assist, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an effective way to recommend calls to the contact center managers that are more likely to contain coachable moments. Our experimental findings demonstrate the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 14 of its 23 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

14 of 23 references.

References · 14

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2 more
  1. 2022Developing a Production System for Purpose of Call Detection in Business Phone Conversationscited by 6
  2. 2022Entity-level Sentiment Analysis in Contact Center Telephone Conversationscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 4.

8 papers.

  1. 2023Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspectivecited by 34
  2. 2023Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMscited by 23
  3. 2025AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agentscited by 4
  4. 2026From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agentscited by 4
  5. 2026Counterfactual Fairness Evaluation of LLM-Based Contact Center Agent Quality Assurance Systemcited by 2
  6. 2025Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generationcited by 1
  7. 2026Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIscited by 1
  8. 2026When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Successcited by 1

Similar, not cited

Close in wording, no citation either way

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  2. 2025Redefining CX with Agentic AI: Minerva CQ Case Study
  3. 2026SETU: An Agentic Ecosystem for Multilingual, Persona-Aware Communication Coaching
  4. 2025Cloning a Conversational Voice AI Agent from Call,Recording Datasets for Telesales
  5. 2024Automated test generation to evaluate tool-augmented LLMs as conversational AI agents
  6. 2025Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents
  7. 2025tAIfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback
  8. 2024Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?