# Combining Cognitive and Generative AI for Self-explanation in Interactive AI Agents

arXiv: https://arxiv.org/abs/2407.18335 · HTML: https://mlsanity.com/paper/2407.18335/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Principles of Explanation in Human-AI Systems](https://mlsanity.com/paper/2102.04972/) (2021)
- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [Self-explaining AI as an alternative to interpretable AI](https://mlsanity.com/paper/2002.05149/) (2020)
- [Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bank](https://mlsanity.com/paper/2307.09369/) (2023)
- [Explanation as Question Answering based on a Task Model of the Agent's Design](https://mlsanity.com/paper/2206.05030/) (2022)

## Built on by (most cited first, 3 of 3)

- [Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI](https://mlsanity.com/paper/2504.07463/) (2025)
- [Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach](https://mlsanity.com/paper/2506.15512/) (2025)
- [Knowledge Model Prompting Increases LLM Performance on Planning Tasks](https://mlsanity.com/paper/2602.03900/) (2026)

## Abstract (arXiv)

The Virtual Experimental Research Assistant (VERA) is an inquiry-based learning environment that empowers a learner to build conceptual models of complex ecological systems and experiment with agent-based simulations of the models. This study investigates the convergence of cognitive AI and generative AI for self-explanation in interactive AI agents such as VERA. From a cognitive AI viewpoint, we endow VERA with a functional model of its own design, knowledge, and reasoning represented in the Task--Method--Knowledge (TMK) language. From the perspective of generative AI, we use ChatGPT, LangChain, and Chain-of-Thought to answer user questions based on the VERA TMK model. Thus, we combine cognitive and generative AI to generate explanations about how VERA works and produces its answers. The preliminary evaluation of the generation of explanations in VERA on a bank of 66 questions derived from earlier work appears promising.