# From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interaction

arXiv: https://arxiv.org/abs/2405.15051 · HTML: https://mlsanity.com/paper/2405.15051/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2024.

## Built on by (most cited first, 26 of 26)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation](https://mlsanity.com/paper/2410.16547/) (2024)
- [Children's Mental Models of AI Reasoning: Implications for AI Literacy Education](https://mlsanity.com/paper/2505.16031/) (2025)
- [GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design](https://mlsanity.com/paper/2508.15227/) (2025)
- [InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills](https://mlsanity.com/paper/2603.07516/) (2026)
- [Explainable AI Systems Must Be Contestable: Here's How to Make It Happen](https://mlsanity.com/paper/2506.01662/) (2025)
- [See What I Mean? CUE: A Cognitive Model of Understanding Explanations](https://mlsanity.com/paper/2506.14775/) (2025)
- [Psychological and behavioural responses in human-agent vs. human-human interactions: a systematic review and meta-analysis](https://mlsanity.com/paper/2509.21542/) (2025)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods](https://mlsanity.com/paper/2607.14123/) (2026)
- [Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products](https://mlsanity.com/paper/2503.17955/) (2025)
- [AdvisingWise: Supporting Academic Advising in Higher Education Settings Through a Human-in-the-Loop Multi-Agent Framework](https://mlsanity.com/paper/2511.05706/) (2025)
- [Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies](https://mlsanity.com/paper/2604.15607/) (2026)
- [Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models](https://mlsanity.com/paper/2409.12840/) (2024)
- [Human Guided Learning of Transparent Regression Models](https://mlsanity.com/paper/2502.15992/) (2025)
- [Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology](https://mlsanity.com/paper/2504.04833/) (2025)
- [Explanation User Interfaces: A Systematic Literature Review](https://mlsanity.com/paper/2505.20085/) (2025)
- [IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems](https://mlsanity.com/paper/2506.21310/) (2025)
- [Leveraging Generative AI for Human Understanding: Meta-Requirements and Design Principles for Explanatory AI as a new Paradigm](https://mlsanity.com/paper/2508.06352/) (2025)
- [Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems](https://mlsanity.com/paper/2508.10806/) (2025)
- [AI as intermediary in modern-day ritual: An immersive, interactive production of the roller disco musical Xanadu at UCLA](https://mlsanity.com/paper/2511.06195/) (2025)
- [It's LIT! Reliability-Optimized LLMs with Inspectable Tools](https://mlsanity.com/paper/2511.14903/) (2025)
- [GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity](https://mlsanity.com/paper/2511.17443/) (2025)
- [An Analytical Multiple Criteria Framework for Temporal and Dynamic Business-to-Business Customer Segmentation in Manufacturing](https://mlsanity.com/paper/2605.17151/) (2026)
- [We Need Explanation Cards to Connect Explanation Algorithms to the Real World](https://mlsanity.com/paper/2606.16786/) (2026)
- [TailorCoPilot: Enabling Agentic Pattern Making with Version-Controlled State Tracking](https://mlsanity.com/paper/2608.25462/) (2026)

## Abstract (arXiv)

AI systems are increasingly being adopted across various domains and application areas. With this surge, there is a growing research focus and societal concern for actively involving humans in developing, operating, and adopting these systems. Despite this concern, most existing literature on AI and Human-Computer Interaction (HCI) primarily focuses on explaining how AI systems operate and, at times, allowing users to contest AI decisions. Existing studies often overlook more impactful forms of user interaction with AI systems, such as giving users agency beyond contestability and enabling them to adapt and even co-design the AI's internal mechanics. In this survey, we aim to bridge this gap by reviewing the state-of-the-art in Human-Centered AI literature, the domain where AI and HCI studies converge, extending past Explainable and Contestable AI, delving into the Interactive AI and beyond. Our analysis contributes to shaping the trajectory of future Interactive AI design and advocates for a more user-centric approach that provides users with greater agency, fostering not only their understanding of AI's workings but also their active engagement in its development and evolution.