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

arXiv: https://arxiv.org/abs/2305.17619 · HTML: https://mlsanity.com/paper/2305.17619/

## Key facts

- Cited by 8 papers in the map (data through 14 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations](https://mlsanity.com/paper/2205.04438/) (2022)
- [An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts](https://mlsanity.com/paper/2209.13736/) (2022)
- [Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop](https://mlsanity.com/paper/2211.01354/) (2022)
- [Developing a Production System for Purpose of Call Detection in Business Phone Conversations](https://mlsanity.com/paper/2205.06904/) (2022)
- [Entity-level Sentiment Analysis in Contact Center Telephone Conversations](https://mlsanity.com/paper/2210.13401/) (2022)

## Built on by (most cited first, 8 of 8)

- [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023)
- [Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs](https://mlsanity.com/paper/2311.00681/) (2023)
- [AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents](https://mlsanity.com/paper/2510.08149/) (2025)
- [From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents](https://mlsanity.com/paper/2605.15104/) (2026)
- [Counterfactual Fairness Evaluation of LLM-Based Contact Center Agent Quality Assurance System](https://mlsanity.com/paper/2602.14970/) (2026)
- [Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation](https://mlsanity.com/paper/2508.18210/) (2025)
- [Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs](https://mlsanity.com/paper/2605.21027/) (2026)
- [When Better Turns Do Not Make Better Agents: Diagnosing the Gap Between Next-Turn Metrics and Workflow Success](https://mlsanity.com/paper/2609.21187/) (2026)

## 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.