# Assessing the Applicability of Existing Design Recommendations to AI Companion Design: A Multi-Method Study

arXiv: https://arxiv.org/abs/2609.14236 · HTML: https://mlsanity.com/paper/2609.14236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 76 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 13 of 13)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships](https://mlsanity.com/paper/2410.20130/) (2024)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Evaluating Quality of Chatbots and Intelligent Conversational Agents](https://mlsanity.com/paper/1704.04579/) (2017)
- [Longitudinal Study on Social and Emotional Use of AI Conversational Agent](https://mlsanity.com/paper/2504.14112/) (2025)
- [Developing a Personality Model for Speech-based Conversational Agents Using the Psycholexical Approach](https://mlsanity.com/paper/2003.06186/) (2020)
- [Digital Companionship: Overlapping Uses of AI Companions and AI Assistants](https://mlsanity.com/paper/2510.15905/) (2025)
- [Towards User-Centric Guidelines for Chatbot Conversational Design](https://mlsanity.com/paper/2301.06474/) (2023)
- [How to Assess Trustworthy AI in Practice](https://mlsanity.com/paper/2206.09887/) (2022)

## Abstract (arXiv)

With the rapid proliferation of large language model (LLM)-based systems, AI companions have emerged as conversational agents designed to cultivate emotional connection rather than primarily to support humans in instrumental tasks. Because engagement with AI companions involves relational, emotional, and potentially long-term interactions, their design is consequential. Prior work has offered guidance for designing trustworthy and relational AI systems and has begun to examine design for AI companionship. However, while such work provides insights into possible design solutions, less is known about what makes AI companion design difficult as a design problem. To examine this challenge, we assessed the applicability of existing design recommendations from adjacent domains in the context of AI companion design. Our multi-method investigation unfolded across four phases: literature review, practitioner co-analysis, internal heuristic evaluation, and external expert assessment. Throughout this process, we synthesized nine design principle areas that surfaced tensions in the applicability of existing recommendations to AI companion design. Our findings show that ethical and UX-oriented considerations are deeply intertwined and often require context-sensitive application. We document a systematic, multi-method problem analysis that uses these principle areas as an analytic artifact to examine why existing recommendations cannot be directly transferred to AI companion contexts.