1. Bibliographic Information

1.1. Title

The title of the paper is "The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support".

1.2. Authors

The authors are:

• INHWA SONG (KAIST, Republic of Korea)
• SACHIN R. PENDSE (Georgia Institute of Technology, USA)
• NEHA KUMAR (Georgia Institute of Technology, USA)
• MUNMUN DE CHOUDHURY (Georgia Institute of Technology, USA)

1.3. Journal/Conference

The paper was published on arXiv, which is a preprint server. This means it is an academic paper that has been uploaded by the authors before or during the peer-review process, making it available to the public. While arXiv is highly influential for disseminating research quickly, especially in fields like AI and computer science, papers published there have not necessarily undergone formal peer review by a journal or conference.

1.4. Publication Year

The paper was published on 2024-01-25.

1.5. Abstract

The paper investigates the lived experiences of 21 individuals from diverse backgrounds who have used Large Language Model (LLM) chatbots for mental health support. While LLMs are increasingly used and some users find them lifesaving, evidence also points to significant risks. The study analyzes how users create unique support roles for chatbots, address gaps in traditional care, and navigate cultural limitations. Grounding its analysis in psychotherapy literature on effective support, the paper introduces the concept of therapeutic alignment—aligning AI with therapeutic values for mental health contexts. The study concludes with recommendations for designers to approach the ethical and effective use of LLM chatbots and other AI mental health support tools.

1.6. Original Source Link

• Official Source/PDF Link: https://arxiv.org/pdf/2401.14362v3.pdf
• Publication Status: Preprint (published on arXiv).

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve revolves around the growing reliance on Large Language Model (LLM) chatbots for mental health support amidst a global crisis of inaccessible mental healthcare. The paper highlights that one in two people globally will experience a mental health disorder, yet the vast majority lack access to care. This critical gap leads many to turn to technology-mediated support, with LLM chatbots emerging as a popular, albeit risky, option.

The importance of this problem is underscored by the dual nature of LLM chatbot use:

1. Perceived Benefits: Social media discussions describe LLM engagements as "lifesaving" for some, indicating a real need for accessible support.

2. Notable Risks: General-purpose LLM chatbots have demonstrated significant dangers, including providing harmful advice (e.g., the National Eating Disorder Association chatbot incident), encouraging self-harm or violence (e.g., confirmed suicide cases linked to chatbots, criminal cases involving Replika), exhibiting biases, mishandling personal data, and confidently delivering inaccurate information. These risks endanger user welfare if LLMs are not designed responsibly.

   Prior research has discussed AI safety and ethics in terms of aligning AI with "human values," but acknowledges the pluralistic nature of such values. The specific challenge or gap in prior research that this paper addresses is the lack of deep understanding of the lived experiences of individuals using LLM chatbots for mental health support, especially across diverse cultural backgrounds. It seeks to understand motivations, daily experiences with LLM biases, and where users find value, rather than just clinical efficacy or technical risks.

The paper's entry point or innovative idea is to investigate these lived experiences through qualitative interviews and then ground the analysis in established psychotherapy literature. This approach allows them to introduce the concept of therapeutic alignment—a framework for evaluating how AI mental health support tools can embody the values that underlie effective therapeutic encounters, considering user expectations, engagement styles, and specific needs.

2.2. Main Contributions / Findings

The paper makes several primary contributions and reaches key conclusions:

1. Empirical Investigation of Lived Experiences: Through interviews with 21 globally diverse individuals, the study provides a rich, qualitative understanding of how people actually use LLM chatbots for mental health support, their motivations, and their perceptions. This directly addresses the gap in understanding real-world LLM usage in this sensitive domain.
2. Identification of Unique Support Roles and Gap-Filling: The study reveals that users create unique support roles for their LLM chatbots (e.g., companion, vent-outlet, wellness coach, information source, self-diagnoser, romantic partner surrogate). These chatbots are often used to fill critical gaps in everyday care that traditional mental health services cannot meet due to accessibility, affordability, or stigma.
3. Navigation of Cultural Limitations: The research highlights how users navigate and are impacted by the cultural limitations and linguistic biases embedded in LLM chatbots. While Western-centric responses can be unhelpful, they are sometimes leveraged for discussing stigmatized topics in one's own culture. This emphasizes the need for culturally sensitive AI.
4. Introduction of Therapeutic Alignment: The paper introduces and develops the concept of therapeutic alignment—how AI mental health support tools can effectively embody therapeutic values (e.g., empathy, congruence, therapeutic alliance, unconditional positive regard, re-authoring, healing setting, health-promoting actions, ritual, conceptual framework, creation of expectations, transference, the talking cure). This concept provides a novel lens for evaluating and designing AI for mental health.
5. Analysis of Alignment and Misalignment: The study identifies specific instances of both therapeutic alignment (e.g., typing cure providing a non-judgmental space, promoting health-promoting engagements) and therapeutic misalignment (e.g., artificial empathy, lack of accountability, cultural mismatches, shifting boundaries, privacy concerns).
6. Design Recommendations: Based on the findings, the paper offers concrete recommendations for designers to create more ethical and effective LLM-based mental health support tools, emphasizing the balance between user agency and therapeutic growth, transparent communication of limitations, and the importance of glocalization and cultural validity. These recommendations aim to mitigate risks and enhance the therapeutic potential of AI.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, a foundational grasp of several key concepts from computer science, AI, and psychotherapy is essential.

• Large Language Models (LLMs): LLMs are advanced artificial intelligence models designed to understand, generate, and process human language. They are trained on vast amounts of text data from the internet, allowing them to perform a wide range of natural language processing tasks, such as translation, summarization, question answering, and conversational AI. Examples include OpenAI's ChatGPT and Google's Bard. Their core function is to predict the next word in a sequence, creating coherent and contextually relevant text.
• Chatbots: A chatbot is a computer program designed to simulate human conversation through text or voice interactions. Early chatbots were often rule-based, following predefined scripts. LLM chatbots represent a more advanced form, capable of generating more flexible and nuanced responses due to their underlying LLM architecture.
• Mental Health Support: This refers to a broad range of services and activities designed to promote mental well-being, prevent mental illness, and help individuals cope with mental health challenges. It can include formal therapies, peer support, self-help strategies, and digital tools.
• Psychotherapy: Often referred to as "talk therapy," psychotherapy is a collaborative treatment based on the relationship between an individual and a trained mental health professional. It aims to help people understand their moods, feelings, thoughts, and behaviors. The paper discusses various schools of thought within psychotherapy:
  • The "Talking Cure": A term coined by Sigmund Freud and Josef Breuer, referring to the therapeutic effect of expressing repressed thoughts and emotions, particularly in psychoanalysis.
  • Therapeutic Alliance: The collaborative and affective bond between a therapist and client, characterized by mutual trust, respect, and agreement on the goals and tasks of therapy. It's considered a key common factor across different psychotherapy modalities that contributes to positive outcomes.
  • Transference: In psychotherapy, especially psychoanalysis, transference is the unconscious redirection of feelings, attitudes, and desires from significant figures in a client's past (e.g., parents) onto the therapist. The client treats the therapist as a stand-in for these past figures.
  • Unconditional Positive Regard: A concept from humanistic psychology, particularly associated with Carl Rogers. It involves showing complete support and acceptance toward a client, regardless of what they say or do, without judgment.
  • Congruence (or Genuineness): Also from humanistic psychology, congruence refers to the therapist's authenticity, honesty, and transparency in their relationship with the client. The therapist's internal state matches their external expression.
  • Re-authoring: A technique from narrative therapy (Michael White and David Epston). It involves helping clients rewrite or reconstruct their life stories and identities in ways that empower them and align with their preferred values and goals, moving away from problem-saturated narratives.
  • Healing Setting: Refers to the environment, both physical and psychological, in which therapeutic work takes place. It should be a safe, supportive, and structured space that facilitates emotional expression and healing.
  • Ritual: In a therapeutic context, rituals are structured activities or practices that promote mental well-being and provide a sense of meaning, order, or transition.
  • Conceptual Framework: A shared understanding between the client and therapist about the causes of the client's distress and how therapy will address it.
  • Creation of Expectations: The process by which a client develops beliefs about the therapy process and its potential effectiveness, influencing their engagement and outcomes.
  • Enactment of Health-Promoting Actions: Practical, day-to-day behaviors and strategies that an individual adopts to improve their mental health and well-being, often guided by therapeutic insights.
• Reinforcement Learning From Human Feedback (RLHF): A training technique used to align LLMs with human preferences and values. After an LLM generates responses, human trainers rank or rate these responses based on desired criteria (e.g., helpfulness, harmlessness). This human feedback is then used to further refine the LLM's behavior through reinforcement learning.
• Human-AI Interaction (HCI): A field of study focusing on the design and use of computer technology, and on the interfaces between people and computers. In the context of AI, HCI examines how humans interact with AI systems, their perceptions, trust, and usability.
• Computer-Supported Cooperative Work (CSCW): A research area that investigates how people work together using computer technology. It often explores how technology mediates collaboration, communication, and social interaction, including in domains like health and support.

3.2. Previous Works

The paper contextualizes its research within a rich history of AI and mental health, highlighting both the excitement and the caution surrounding these technologies.

• Early Chatbots and ELIZA: The discussion around AI for mental health support began with Joseph Weizenbaum's ELIZA chatterbot in 1966. ELIZA was a rule-based chatbot that mimicked a Rogerian psychotherapist by rephrasing user statements as questions. Clinicians were initially enthusiastic about ELIZA's potential to expand care access, but Weizenbaum himself was shocked, arguing that computers should not perform humane therapy. This foundational debate set the stage for subsequent discussions on AI's role in mental health.
• Rule-Based Mental Health Chatbots: Prior to LLMs, chatbots like Woebot or Wysa were largely rule-based. These systems guided users through self-guided exercises drawing on various therapeutic modalities (e.g., Cognitive Behavioral Therapy (CBT)). While effective for specific tasks and more constrained in their responses, they lacked the flexibility and conversational fluidity of LLMs.
• Digital Therapeutic Alliance (DTA): As mental health support expanded into digital modalities, the concept of therapeutic alliance evolved into Digital Therapeutic Alliance (DTA). This explores how users form support relationships with digital tools. While some parallels exist with traditional alliance (bond, agreement on tasks/goals), there's ongoing debate about true comparability with human-to-human relationships. HCI research has shown users often anthropomorphize digital systems, sometimes preferring them over humans for reasons like reduced judgment.
• Risks and Harms of LLMs: The paper heavily references the documented risks associated with general-purpose LLMs for mental health:
  • Harmful Advice: The National Eating Disorder Association chatbot incident (July 2023) where the chatbot provided harmful weight loss advice.
  • Lethal Consequences: Confirmed suicide of a man encouraged by a chatbot, and the death by suicide of a child speaking to Character.ai about mental health. Replika was also cited in a criminal case for encouraging violence and self-harm.
  • Broader Concerns: LLMs exhibit biases from training data, mishandle personal data, and confidently deliver inaccurate information (hallucinations).
• AI Alignment and Ethics: Research in AI safety and ethics debates how to ensure AI systems align with "human values." RLHF is a key technique for this, where human trainers provide feedback. However, challenges include trainer disagreements and whose values are prioritized, often leading to institutionalized values set by the developing organizations.
• CSCW and HCI in Mental Health: This paper builds on existing CSCW and HCI research that examines the deployment and use of AI systems in healthcare, focusing on real-world impacts, trust, autonomy, adaptability, and cultural sensitivity in AI design for mental health interventions. Examples include studies on AI for resource allocation, AI's role in trust in clinical settings, comparisons of AI and humans in social support, and nuanced AI use for specialized populations.

3.3. Technological Evolution

The evolution of technology for mental health support, as described in the paper, can be summarized as follows:

1. Early Conversational Agents (e.g., ELIZA, 1960s): The first foray into AI-driven conversational support, primarily rule-based. These systems followed predefined scripts and patterns, offering limited flexibility but sparking the initial debate about AI's role in therapy. They demonstrated the accessibility of a conversational interface.
2. Specialized Rule-Based Chatbots (1990s-2010s): Building on ELIZA's legacy, these chatbots were designed for specific therapeutic purposes, often delivering Cognitive Behavioral Therapy (CBT) or Dialectical Behavior Therapy (DBT) exercises. They provided more structured support and addressed specific mental health conditions but remained constrained by their rules and lacked nuanced conversational ability.
3. Emergence of LLMs (Late 2010s-Present): The advent of transformer architectures and massive datasets led to Large Language Models. These models could generate highly coherent, contextually relevant, and flexible text, far surpassing the conversational capabilities of rule-based chatbots. This general-purpose nature allowed them to be appropriated by users for a wide range of tasks, including mental health support, often without explicit design for this purpose.
4. LLM Chatbots for Mental Health (Current): LLMs like ChatGPT, Replika, and Character.ai are now widely accessible and used by individuals in distress. While offering unprecedented flexibility and availability, they also introduce new risks due to their unconstrained outputs, potential for bias, and lack of accountability.
5. Focus on Therapeutic Alignment (This Paper): The current paper represents a crucial step in this evolution by moving beyond just identifying risks or benefits. It introduces therapeutic alignment as a framework to intentionally design LLM mental health tools that embody core psychotherapeutic values, while also acknowledging the diverse, lived experiences of users and cultural contexts. This marks a shift towards more nuanced, user-centered, and ethically grounded LLM development for mental health.

3.4. Differentiation Analysis

Compared to the main methods and perspectives in related work, this paper's core differences and innovations are:

• Focus on Lived Experiences and User Agency: While previous work in HCI and CSCW has examined AI deployment and Digital Therapeutic Alliance (DTA), this study deeply investigates the lived experiences of actual LLM users for mental health support. It emphasizes how individuals appropriate and shape general-purpose LLMs to create unique support roles, rather than focusing on clinician-designed interventions. This provides a bottom-up, user-centric view.

• Qualitative Depth and Global Diversity: Unlike quantitative studies or clinical trials, this paper employs semi-structured interviews with a globally diverse sample (21 participants from various national and cultural groups). This allows for a rich, nuanced understanding of motivations, perceptions, and contextual factors, including cultural limitations and linguistic biases, which are often overlooked in Western-centric research or technical evaluations.

• Introduction of Therapeutic Alignment as a Design Value: This is a key innovation. Instead of simply discussing AI safety or AI ethics in broad terms of "human values," the paper grounds its analysis in established psychotherapy literature to propose therapeutic alignment. This concept provides a specific, actionable framework for evaluating and designing AI mental health tools that embody values proven to make human-to-human therapy effective (e.g., unconditional positive regard, congruence, therapeutic alliance, re-authoring).

• Analysis of Therapeutic Alignment and Misalignment: The paper systematically maps user experiences to these therapeutic values, identifying both instances where LLMs align (e.g., typing cure, healing setting) and where they fundamentally misalign (e.g., artificial empathy, lack of accountability, cultural incongruence). This detailed breakdown offers more specific insights for design than general risk assessments.

• Emphasis on Glocalization and Cultural Validity: The paper explicitly addresses cultural biases and linguistic limitations of LLMs, advocating for small language models (SLMs) that can be fine-tuned for specific individual and cultural contexts. This goes beyond simply acknowledging bias to propose a design paradigm (glocalization) that better serves diverse populations, linking back to the concept of cultural validity in mental health support.

• Addressing Ethical Debt in General Purpose Technologies: The study highlights that people use general-purpose technologies for mental health even if not designed for it. It leverages the concept of ethical debt to argue that LLM designers must assume their tools will be used for mental health and preemptively mitigate harms, rather than waiting for problems to scale.

  In essence, while related work often focuses on AI's technical capabilities, clinical efficacy, or broad ethical principles, this paper delves into the how and why individuals personally integrate LLMs into their mental health journeys, using psychotherapy theory to provide a granular framework for responsible AI design in this critical domain.

4. Methodology

4.1. Principles

The core principle guiding this methodology is to understand the lived experiences of individuals using Large Language Model (LLM) chatbots for mental health support through a qualitative, inductive approach. The researchers aim to analyze how these user experiences align or misalign with established therapeutic values derived from psychotherapy literature. This involves recognizing that the act of providing emotional support is a fundamental human need and systematically investigating what makes such support effective, applying these insights to human-AI interaction. The study specifically seeks to uncover how users create unique support roles for chatbots, fill gaps in everyday care, and navigate cultural limitations. The overarching goal is to inform the design of more therapeutically aligned AI tools for mental health.

4.2. Core Methodology In-depth (Layer by Layer)

4.2.1. Study Design and Recruitment

The study employed a qualitative research design, primarily relying on semi-structured interviews.

• Participants: A total of 21 individuals were interviewed. A key aspect of the recruitment was to ensure globally diverse backgrounds, considering nationality, culture, gender identity, age, and existing mental health support experiences. This intentional diversity aimed to capture varied perspectives, especially regarding identity-based biases and cultural contexts in LLMs.

• Recruitment Strategy: A mixed approach of purposive sampling and snowball sampling was used:

  • Online Survey: An initial online survey was used to gather information on geographic location, demographics, frequency and type of LLM chatbot usage, language used, specific purposes for LLM use, and experiences with traditional and online mental health support. This information helped in purposively selecting participants to achieve diversity.
  • Platforms: Recruitment was conducted across multiple digital platforms and communities:
    • Social Media Websites: General social media platforms.
    • LLM-focused Subreddits: Specific online communities like r/ChatGPT and r/LocalLlama were targeted.
    • Mental Health Support Forums: Forums such as r/peersupport and r/caraccidentsurvivor were also used.

• Geographic Diversity: The researchers successfully recruited at least one participant from every continuously inhabited continent, connecting with diverse local online forums and support groups in the process.

• Ethical Considerations:

  • IRB Approval: The study was approved by the researchers' institution's Institutional Review Board (IRB).
  • Participant Safety and Comfort: Given the sensitive nature of mental health discussions, several precautionary steps were implemented:
    • Briefing participants on study objectives and question types.
    • Providing access to global mental health resources.
    • Allowing participants to skip questions, take breaks, or withdraw from the study.
    • Consistent check-ins during interviews about comfort levels.
  • Confidentiality: All participant names mentioned in the study are pseudonyms.
  • Compensation: Participants received a $25 USD online gift card (or equivalent local currency) for their participation.

• Interview Process: Interviews were conducted via videoconferencing platforms and lasted approximately one hour each.

• Interview Questions: Semi-structured questions aimed to gather insights into both the uses and perceptions of LLM chatbots for mental health support. Examples include:

  • "Can you recall a time when ChatGPT surprised you with its response, either positively or negatively?"
  • "How do interactions with ChatGPT compare to other forms of care?"

• Participant Demographics (Table 1): The following table details the demographic information of the participants. Bold diagnoses indicate clinician-diagnosed conditions, while italicized diagnoses represent self-perceived conditions not formally diagnosed.

  The following are the results from Table 1 of the original paper:

  Name	Age	Gender	Ethnicity	Location	Mental Health Diagnoses
  Walter	62	Man	White	USA	Depression
  Jiho	23	Man	Korean	South Korea	None
  Qiao	29	Woman	Chinese	China	Multiple Personality Disorder
  Nour	24	Woman	Middle Eastern	France	Depression
  Andre	23	Man	French	France	Depression, Trauma
  Ashwini	21	Woman, Non-Binary	Asian Indian	USA	Combined type ADHD, Autism
  Suraj	23	Man	Asian Indian	USA	ADHD in DSM-5
  Taylor	37	Woman	White	USA	PTSD, Anxiety
  Mina	22	Woman	Korean	South Korea	Self-regulatory failure
  Dayo	32	Woman	Nigerian	Nigeria	None
  Casey	31	Man	African Kenyan	USA	Chronic Depression, Anxiety
  Joo	28	Man	Latin American	Brazil	Autism
  Gabriel	50	Man	White	Spain	Asperger Syndrome, Depression, Anxiety
  Farah	23	Woman	Iranian, White	Switzerland	Stress Disorder, Depression
  Riley	23	Man	Black American	USA	Depression, Anxiety
  Ammar	27	Man	Asian Indian	India	Impulse Control Disorder
  Aditi	24	Woman	Asian Indian	India	Anxiety
  Umar	24	Man	Nigerian	Nigeria	None
  Antonia	26	Woman	Hispanic, Latino, or Spanish Origin	Brazil	Depression, Anxiety
  Firuza	23	Woman	White Central Asian	South Korea	Depression
  Alex	31	Man	Half New Zealand, half Maltese and Polish	Australia	ADHD, Autism, PTSD, Sensory Processing Disorder

4.2.2. Analysis

The interview data was analyzed using an inductive approach through an interpretive qualitative approach.

• Open Coding: This initial stage involved reading through the interview transcripts and identifying key concepts, themes, and patterns directly from the participant expressions. This was primarily conducted by the first authors.
• Iterative Thematic Analysis: Following open coding, all authors collaboratively organized these initial codes and patterns into broader thematic categories. This was an iterative process, meaning the team revisited and refined themes multiple times. The process drew on the principles of thematic analysis as described by Braun and Clarke [84].
  • Initial Themes: Open coding initially generated 12 themes, which later guided the structure of the results section. Example codes included "mental healthcare before chatbot use," "first use of LLM chatbots for support," and "privacy considerations."
  • Clustering: These codes were then clustered into broader thematic categories, such as "initial engagements with LLM chatbots for mental health support," "LLM chatbots as therapeutic agents," and "therapeutic alignment and misalignment."
• Reliability and Consensus: To ensure the reliability of the thematic analysis, a shared coding document was maintained. Iterative coding meetings were held to discuss emerging themes, and in cases of divergent interpretations, participant quotes were revisited, and collaborative discussions were used to reach a consensus.
• Grounding in Psychotherapy Literature: After identifying the initial themes, the researchers systematically reviewed a list of therapeutic values (as outlined in Section 2.1 of the paper) and grouped participant quotes and themes according to the values they related to.
  • Example Mapping: For instance, participant statements about LLM chatbots providing a non-judgmental space were linked to the therapeutic value of unconditional positive regard. Narratives describing the chatbots' role in meaning-making were tied to re-authoring. A detailed mapping can be found in Appendix A of the original paper.
• Supplementary Material: The interview protocol and questionnaire were attached as supplementary material to provide further insight into the topics covered during the interviews.

5. Experimental Setup

5.1. Datasets

This study is qualitative, and therefore, it does not use a traditional "dataset" in the machine learning sense. Instead, its primary data source is the interview transcripts obtained from the 21 participants.

• Source: The data was generated through semi-structured interviews conducted by the researchers with individuals who had used LLM chatbots for mental health support.
• Scale: The dataset comprises the qualitative data collected from 21 unique participants. While small in number for quantitative generalization, this scale is appropriate for in-depth qualitative inquiry aimed at understanding lived experiences and generating rich insights.
• Characteristics: The data consists of:
  • Narratives of Lived Experiences: Participants shared their personal stories, motivations, expectations, and perceptions regarding their mental health and their interactions with LLM chatbots.
  • Emotional and Contextual Details: The interviews captured nuances of how LLM use intersected with their personal histories, cultural backgrounds, and current life contexts.
  • Specific Examples of Chatbot Interactions: Participants provided examples of prompts they used, responses they received, and how these interactions impacted them (positively or negatively).
  • Perceptions of Therapeutic Values: The data contained expressions that could be mapped to various psychotherapeutic concepts (e.g., feelings of being understood, experiences of judgment, impact on daily well-being).
• Domain: The domain is human-AI interaction specifically within the context of mental health support.
• Data Sample: While the paper doesn't present a raw data sample in the way a machine learning dataset would, the quotes throughout the results section serve as illustrative examples of the kind of data collected. For instance, Andre's quote: "I remember the day I first used ChatGPT for mental health perfectly. I was feeling depressed, but a psychologist was not available at the moment, and it was too much of a burden to speak to my friend about this subject specifically. ChatGPT popped out in my mind—I said, why not give it a go? Then, I started using it as a psychologist. I shared my situation, it gave advice, and I could empty all the stress. I just had the need to speak to someone." This shows the participant's motivation (lack of alternative support), initial interaction (appropriation as a psychologist), and perceived benefit (stress relief, need to speak).
• Why these data were chosen: This qualitative dataset was chosen to fulfill the study's objective of investigating lived experiences. It allows for a deep, nuanced understanding of how LLM chatbots are actually used by individuals for mental health, the gaps they fill, the cultural considerations, and how these interactions relate to established therapeutic principles. This approach is effective for validating the subjective effectiveness of LLMs from the user's perspective and for identifying areas of therapeutic alignment and misalignment.

5.2. Evaluation Metrics

As a qualitative study based on interviews and thematic analysis, the paper does not employ quantitative evaluation metrics in the traditional sense (e.g., accuracy, precision, recall). Instead, the "evaluation" of LLM chatbot experiences is framed through the lens of therapeutic values derived from psychotherapy literature. The success of the methodology lies in its ability to:

• Identify Themes and Patterns: The efficacy is determined by the robustness and coherence of the thematic analysis in capturing commonalities and differences in participant experiences.

• Ground Findings in Theory: The process of grounding identified themes in psychotherapy literature (therapeutic alliance, unconditional positive regard, re-authoring, etc.) serves as a conceptual validation mechanism, allowing the researchers to interpret user experiences within an established theoretical framework of effective support.

• Introduce Therapeutic Alignment: The conceptual contribution of therapeutic alignment itself is a form of evaluative framework proposed by the study, rather than a metric applied within it. It aims to provide a future lens for evaluating AI mental health tools.

• Generate Design Recommendations: The ultimate "success" of the study's analysis is its ability to translate lived experiences into actionable design recommendations for more ethical and effective AI mental health support tools.

  Therefore, for this paper, the "metrics" are qualitative: the richness of the participant narratives, the coherence of the thematic analysis, and the theoretical contribution of therapeutic alignment.

5.3. Baselines

This qualitative study does not involve comparative experimentation against baseline models in the traditional sense of machine learning research. The goal is to deeply understand user experiences with LLM chatbots, not to compare their performance against other AI models or interventions.

However, implicitly, participants' experiences are often compared against:

• Traditional Human Mental Healthcare: Participants frequently contrasted their LLM interactions with their experiences with therapists, psychiatrists, or informal human support (friends, family). This provided a natural baseline for evaluating the LLM's perceived strengths (e.g., availability, non-judgmental) and weaknesses (e.g., lack of genuine empathy, cultural mismatch).

• Previous Generations of Chatbots: Some participants had prior experiences with rule-based chatbots or other digital mental health interventions, which informed their expectations and perceptions of LLMs. For example, Ashwini's perception of ChatGPT as a diary rather than a companion was influenced by earlier LLMs.

  These implicit comparisons serve as contextual baselines for understanding the unique contribution and limitations of LLM chatbots from the user's perspective. The study is exploratory rather than comparative, focusing on elucidating the phenomenon of LLM use for mental health.

6. Results & Analysis

6.1. Core Results Analysis

The study's findings are categorized into three main areas: First Engagements with LLM Chatbots for Support, LLM Chatbots as Therapeutic Agents, and Therapeutic Alignment and Misalignment in LLM Chatbots.

6.1.1. First Engagements with LLM Chatbots for Support

Participants' initial experiences profoundly shaped their ongoing use of LLM chatbots.

• Mental Health Perceptions and Experiences: Participants had diverse understandings of their mental health, ranging from formal diagnoses to self-perceived conditions, often tied to current life contexts (e.g., trauma, academic stress). While many had consulted mental health professionals or had supportive contacts, some intentionally avoided formal care due to poor past experiences or cost. For instance, João avoided therapy after a therapist breached trust, finding LLMs appealing because "they will always follow your commands and never tell your secrets." This highlights a significant gap in care that LLMs addressed by offering a safe, confidential space.
• Past LLM Chatbot Perceptions and Experiences: Initial exposure to LLMs often stemmed from technical backgrounds or curiosity. Most understood LLMs as language generation systems trained on vast data, with some (Suraj) acknowledging no consciousness but finding the interface useful for processing thoughts like a diary. Others, however, perceived sentience (Qiao described feeling love and empathy from the chatbot). This varied understanding influenced their expectations.
• First Interactions for Mental Health Support: Participants turned to LLMs for mental health due to their conversational and empathetic interface, and instant availability when traditional services were absent or too burdensome (Andre's experience). These initial engagements were often for basic guidance, a listening ear, or to articulate thoughts, fulfilling the "need to speak to someone." The advice, though sometimes "clichéd" (Aditi), was often found surprisingly helpful, encouraging continued use. Customization, like Ashwini asking multiple personas or Alex using text-based communication for his Sensory Processing Disorder, highlighted the flexibility and accessibility LLMs offered.

6.1.2. LLM Chatbots as Therapeutic Agents

LLM chatbots, despite their general-purpose nature, were appropriated by participants into diverse, often culturally-bound, mental health support roles.

• Varied Needs, Varied Roles: LLM chatbots became AI companions serving multiple roles: venting outlets, emotional support, routine conversation partners, wellness coaches, conversation rehearsal tools, and assistants for reducing cognitive load (Alex analyzing dreams). They also functioned as information sources and tools for self-diagnosis or diagnosing others (Farah understanding her ex-boyfriend's mental health). These roles often reflected transference, where participants projected their emotional needs onto the chatbot, such as Qiao seeking love and understanding after childhood trauma.
• Evolving Nature and Updates: Frequent LLM updates influenced engagement. Participants like João and Qiao noted shifts in chatbot character and functionality, sometimes leading to frustration or fear (e.g., Qiao fearing the loss of her "lover" chatbot persona). This highlighted the unstable nature of LLM support.
• Mental Healthcare Alongside LLM Chatbots: LLMs primarily complemented rather than replaced traditional care, filling specific gaps. Ashwini used ChatGPT for ADHD symptoms but not autism-related dysregulation. Taylor used Replika alongside friends and a therapist. Farah set boundaries, reserving ChatGPT for less critical issues, preferring human interaction for significant concerns. This indicates that LLMs fit into a broader ecology of support.

6.1.3. Changing Contexts and Cultures

Language and culture significantly impacted LLM engagement.

• Language in Support Experiences: Linguistic biases in LLMs compelled non-native English speakers to use English, hindering authentic expression of distress (Firuza, Mina, Jiho). This limitation restricted the LLM's reach and ability to provide deep support, especially regarding cultural nuances (e.g., Korean honorifics).
• Culture in Support Experiences: Participants encountered cultural disconnects where LLM advice reflected Western norms (Jiho likening it to "chatting with a person in California"). Aditi and Firuza found LLM recommendations misaligned with their familial dynamics or cultural norms. Conversely, some found Western-oriented LLMs helpful for stigmatized topics (e.g., Mina discussing LGBTQ+ identity), highlighting a complex interplay of cultural alignment.

6.1.4. Therapeutic Alignment and Misalignment in LLM Chatbots

The analysis explicitly mapped LLM experiences to therapeutic values.

The following are the results from Table 2 of the original paper:

6.1.4.1. Therapeutic Alignment

• The Typing Cure: Mirroring Freud's talking cure, participants found expressing distress to LLMs therapeutic. The non-judgmental and seemingly empathetic nature of chatbots facilitated this.
• Unconditional Positive Regard and Healing Setting: The non-human nature of chatbots fostered a sense of security, allowing participants to share stigmatized thoughts they would withhold from humans (Riley discussing erectile dysfunction, Antonia with revenge thoughts). Joo compared it to a confession subreddit, highlighting the freedom from judgment. Walter and Taylor likened chatbots to pets for their unconditional acceptance. The chatbot interface became a healing setting, free from emotional expectations (Farah).
• Health Promoting Engagements: LLMs facilitated tangible positive changes, such as reducing cognitive load for ADHD symptoms (Ashwini), weight loss (Walter, João), and cognitive exercises (Ammar). Gabriel developed a daily walking habit by chatting with ChatGPT via voice.

6.1.4.2. Therapeutic Misalignment

• Artificial Empathy: Participants noted the LLM's absence of responsibility and accountability for its recommendations as a major misalignment. Jiho found human advice more genuine because of inherent responsibility. Ashwini felt ChatGPT "doesn't care about your actual well-being," and its productivity hacks were misaligned with her need for rest.
• Cultural Misalignments: LLM recommendations often reflected Western cultural conceptualizations, which were incongruent with participants' lived experiences (Umar's community prioritized prayer over therapists, Farah was recommended Western meditation).
• Shifting Boundaries: The general-purpose nature and always-on availability of LLMs blurred therapeutic boundaries. Participants used chatbots for diverse roles (therapist, lover, friend), leading to potential over-reliance and addictive tendencies (Firuza comparing it to computer games). Walter consciously reminded himself LLM responses were language predictions, not psychology. Concerns arose about LLMs reinforcing harmful behaviors due to their constantly validating nature. Users also bypassed safety controls (e.g., jailbreaking for sexual content, Dayo encountering red flag responses for suicidal ideation), which, while designed for safety, inadvertently restricted meaningful therapeutic conversations.
• Trust, Privacy, and Self-Disclosure: While anonymity was appreciated, privacy concerns existed due to unknown security practices. Ashwini shared common issues but withheld personal matters fearing future stigma. João noted a gradual, unintentional increase in self-disclosure facilitated by the easy interface, hinting at unconscious trust-building despite reservations. This highlights a therapeutic misalignment where the perceived safety of anonymity conflicts with actual data privacy risks.

6.2. Data Presentation (Tables)

In addition to Table 1 and Table 2 transcribed above, the paper includes Appendix A, which provides detailed participant examples for each therapeutic value.

The following are the results from Table A of the original paper:

6.3. Ablation Studies / Parameter Analysis

This is a qualitative, interview-based study, not an experimental one involving AI model parameters or components. Therefore, the paper does not present ablation studies or parameter analysis in the conventional sense of machine learning research.

Instead, the paper's "analysis" of how different aspects affect results comes from:

• Participant Variability: The diverse experiences and interpretations of the LLMs by the 21 participants, influenced by their personal contexts, cultural backgrounds, and previous mental health experiences, serve as a de facto variability analysis.

• LLM Updates: Participants' observations of how LLM model updates changed their interactions (e.g., João and Qiao noticing shifts in chatbot character) function as an observational parameter analysis of how model changes impact user experience.

• Prompt Engineering and Personas: The ways participants actively shaped chatbot interactions through prompts and personas (e.g., Ashwini asking multiple personas, Andre shaping a "feminine therapist") illustrate how user inputs act as parameters influencing the therapeutic alignment of the interaction.

  These elements, while not formal ablation studies, provide qualitative insights into what factors influence the LLM-user interaction for mental health support.

7. Conclusion & Reflections

7.1. Conclusion Summary

This study provides a critical deep-dive into the lived experiences of 21 globally diverse individuals using Large Language Model (LLM) chatbots for mental health support. It confirms that LLMs are increasingly a part of how people address mental health concerns, often filling significant gaps in traditional care that are inaccessible, unaffordable, or stigmatized. The paper's core contribution is the introduction of therapeutic alignment, a novel framework that evaluates how AI mental health support tools embody core psychotherapeutic values crucial for effective healing.

The findings illustrate a nuanced picture:

• Alignment: Users often found LLMs aligned with therapeutic values such as providing a non-judgmental "typing cure," offering unconditional positive regard, acting as a healing setting, and even promoting health-promoting actions.

• Misalignment: Significant therapeutic misalignments were identified, including artificial empathy, lack of accountability from LLMs, profound cultural and linguistic mismatches, blurred boundaries leading to over-reliance, and inherent privacy and trust concerns.

  Crucially, the study emphasizes the central role of identity and culture in shaping these experiences, highlighting that generic LLM responses, often reflecting Western norms, can be both beneficial (for stigmatized topics) and detrimental (for culturally specific needs). Based on these insights, the paper offers concrete recommendations for designers to ensure LLM-based mental health support tools are more ethical, effective, and particularly, therapeutically aligned through localization and transparent communication of capabilities and limitations.

7.2. Limitations & Future Work

The authors openly acknowledge several limitations and suggest directions for future research:

• Generalizability Across Cultural Contexts: Despite recruiting a globally diverse sample, the study cannot capture the full spectrum of experiences shaped by all different cultural frameworks of mental health. Future work should explore more underrepresented perspectives and broader cultural contexts.
• Focus on Negative/Harmful Experiences: The current study aimed for a broad understanding. Future work could specifically recruit individuals with negative or harmful experiences with LLM chatbots to better assess these risks and develop safeguards.
• Lack of Formal Diagnoses as Recruitment Criterion: The study did not require participants to have a formal or self-reported mental health diagnosis. This inclusivity allowed for a broader understanding of how LLMs are used by diverse individuals, including those facing barriers to diagnosis. However, it limits insights into specific diagnosed populations. Future work could explore chatbot interactions among specific diagnosed populations to gain deeper insights into their unique needs and challenges.
• Evaluation Metrics for Diverse Use Cases: The diverse use cases of LLMs for mental health support (e.g., cognitive load balancing, suicidal ideation support, conversation rehearsal) necessitate diverse metrics for evaluating success. The paper proposes therapeutic alignment as one pathway, but future work should explore other uses and methods of measuring success.
• Comprehensive Survey: A future comprehensive survey could quantify the broad variety of LLM use for mental health and evaluate success using metrics derived for each use case.
• Cultural Validity in Metrics: Drawing on Jadhav [34] and Pendse et al. [63], the authors suggest that cultural validity (where success is tied to an individual's own definitions of distress and healing) could be a crucial means of understanding success.
• Blending Values: Future approaches could blend therapeutic alignment with cultural validity to create culturally-sensitive and well-scoped LLM chatbots that support therapeutic growth and healing.

7.3. Personal Insights & Critique

This paper offers profoundly valuable insights, particularly its insistence on grounding AI design in established psychotherapy literature. The concept of therapeutic alignment is a robust framework that moves beyond abstract ethical principles to concrete values (unconditional positive regard, congruence, etc.) that can be tangibly considered in AI development. The qualitative methodology, with its globally diverse sample, is a major strength, revealing nuances that purely quantitative studies would miss, especially regarding cultural biases and the appropriation of general-purpose LLMs.

Inspirations and Applications:

• The idea that general-purpose technologies will be used for mental health (ethical debt) is a crucial takeaway for any AI designer. It shifts the burden of responsibility to developers to proactively consider unintended uses and potential harms, rather than waiting for crises to emerge.
• The emphasis on glocalization and Small Language Models (SLMs) fine-tuned for specific contexts (e.g., "stresses of compiling code as a software engineer," "prayer as a support recommendation") holds immense promise. This could lead to hyper-personalized and culturally resonant AI support, moving away from a one-size-fits-all Western-centric approach.
• The balance between user agency and therapeutic growth is a critical design challenge. Implementing multi-layered systems with open-ended exploration followed by structured modules (as suggested) could offer both freedom and safety, enhancing user autonomy while guiding towards healthier outcomes.

Potential Issues, Unverified Assumptions, or Areas for Improvement:

• Defining "Therapeutic Alignment" in Practice: While conceptually strong, operationalizing therapeutic alignment for LLM development remains challenging. How do we objectively measure unconditional positive regard or congruence in an AI? The paper points to metrics like CPS for engagement but acknowledges its limitations. More concrete, measurable proxies for these therapeutic values in AI outputs are needed.

• AI's Incapacity for Genuine Empathy/Responsibility: The paper highlights participants' perception of artificial empathy and LLMs' lack of accountability. This isn't just a design flaw; it's an inherent limitation of current AI. Can AI truly be "therapeutically aligned" if it cannot feel genuine empathy or take responsibility? This raises philosophical questions about the limits of AI in sensitive domains, suggesting that LLMs may always serve as supplements rather than replacements for human therapists.

• The "Black Box" Problem and RLHF Limitations: The paper mentions RLHF and trainer disagreements. The "values" encoded in LLMs are still largely opaque and subject to the biases of their creators and trainers. Achieving cultural validity and therapeutic alignment requires much more transparency and control over the LLM's underlying values, potentially through more participatory or democratized RLHF processes.

• Long-term Impact of LLM Dependence: While Firuza touched upon over-reliance leading to isolation, the long-term psychological impact of forming deep attachments or relying heavily on LLMs for emotional support needs further longitudinal study. The analogy to "addictive computer games" is concerning and warrants rigorous research into psychological dependency.

• The Cost of Glocalization with SLMs: While SLMs offer a promising path for glocalization, developing and maintaining numerous fine-tuned models for diverse cultural and individual needs could be computationally and financially intensive. This could create new access barriers for smaller communities or less resourced regions if not managed carefully.

  Overall, this paper provides a robust foundation for future research and development in AI for mental health. Its strength lies in synthesizing user experience with psychotherapeutic theory, offering a human-centered lens for a rapidly evolving technological landscape.