Integrating Artificial Intelligence into Peer Support

Peer supporters are among the most rapidly expanding seg­ments of the overall behavioral health workforce. They have been integrated into a wide variety of programs and settings, including inpatient, residential, outpatient, and crisis re­sponse services. Peer supporters use their own experiences with mental health and substance use challenges to build au­thentic, relationship-centered connections with service users that are designed to empower individuals, foster hope, and promote recovery. In contrast to other clinical relationships, peer support involves the bidirectional sharing of experienc­es, mutual support, and advocacy, components central to ef­fective peer-led interventions.

Despite its increasingly central role in service deliv­ery, however, the peer support workforce has experienced a long-standing digital divide, and the integration of new technology has often lagged behind other areas of behav­ioral health. The authors of a 2026 article (1) explored the recent and rapid emergence of artificial intelligence (AI) technologies, including chatbots, copilots, and AI-driven predictive analytics, and noted how these technologies present both risks and opportunities for the peer support workforce. On the one hand, AI has the potential to increase access to needed care, reduce the administrative burden of peer supporters, and augment peer support interactions. On the other hand, these technologies carry significant risks: without careful implementation, AI may compromise the relational qualities, privacy, and ethical principles that un­derpin peer support.

AI represents rapidly evolving technology, and in this article we describe AI’s roles, benefits, and risks, as well as potential strategies (considerations) to ethically integrate AI into peer support, maintain human-centered connections, and align technology use with the core values of peer support practice. Much of what we discuss in this article could be ap­plied to other roles and provider types; because we were com­missioned to focus explicitly on peer support, our discussion focuses as much as possible on issues directly relevant to the peer support workforce.

The term artificial intelligence refers to computerized systems that perform tasks requiring humanlike pattern recognition, decision support, or language processing through algorithms and machine-learning models. In mental health, AI often appears in the form of conversational agents (chatbots) and other digital tools designed to interact with providers and service users, augmenting training and documentation, providing psychoeducation, flagging nascent issues, and prompting self-reflection. AI-driven tools generate re­sponses grounded in large datasets and algorithmic pattern matching but do not possess genuine (human) lived experi­ence or empathy, and they do not have the capacity for ethical judgment. AI tools can be designed to function independently (in the form of commercially available generative AI [GenAI] chatbots, e.g., ChatGPT, which can be directly accessed via computers or smartphones) or through human mediation.

In mental health, AI often appears in the form of conversational agents (chatbots) and other digital tools designed to interact with providers and service users, augmenting training and documentation, providing psychoeducation, flagging nascent issues, and prompting self-reflection.

In peer support contexts, it is important to distinguish AI-assisted but human-led peer support from stand-alone AI “peer support” agents. Multiple peer support agents have already been developed and reported on in the AI literature; for example, RecoveryTeller has been described as “a chat­bot adopting a recovered-peer persona that portrays itself as someone [who has] recovered from an [eating disorder]” (2). Stand-alone peer support agents have been advocated as a means of facilitating 24/7 access, immediate response, low operational costs, and potential appeal to individuals who might feel shame or embarrassment in disclosing their con­cerns to a human (3).

Stand-alone peer support agents have been advocated as a means of facilitating 24/7 access, immediate response, low operational costs, and potential appeal to individuals who might feel shame or embarrassment in disclosing their concerns to a human.

In contrast to stand-alone AI peer support agents, AI-assisted peer support occurs when human peer supporters use AI tools to augment (human-to-human) interactions while maintaining the centrality of human relationships, for example, by generating reminders, drafting documentation, or aiding in the identification of resources. Although chat­bots can be trained to simulate peer support personas or responses, they cannot in fact function as peer supporters in the fundamentally human, embodied, and experientially grounded sense that peer support has long been understood to represent. Moreover, myriad examples now exist of the ways in which stand-alone GenAI apps, used for the purposes of mental health–related discussion and support, can go cat­astrophically awry. At present, the safety concerns and lack of adequate safeguards are such that we do not believe that Ge­nAI agents that present themselves as peer supporters should be used. Therefore, for the purposes of this article, we discuss benefits, risks, cautions, and considerations to guide the in­tegration of AI into peer support—that is, the integration of AI-driven tools into the workflows of human peer supporters.

AI offers several potential benefits for augmenting peer sup­port in behavioral health settings, particularly when this tech­nology is integrated thoughtfully alongside human support. One important area is scalability and access. The demand for mental health services con­tinues to outpace the supply of trained providers, leading to gaps in services and long wait times. By reducing pa­perwork and associated ad­ministrative burden (as we describe later), AI assistance may facilitate more face-to-face interactions and increase the capacity to serve a larger number of individuals.

AI offers several potential benefits for augmenting peer support in behavioral health settings, particularly when this technology is integrated thoughtfully alongside human support.

AI-driven tools, including chatbots, can facilitate individual­ly tailored training and feedback before a peer supporter en­gages in direct interactions with service users or participants. For example, simulated-patient chatbots can enable new peer supporters to practice difficult or complicated interactions or responses to challenging scenarios, such as responding to a racist comment made in a peer support group. In some contexts, chatbots may also be able to provide real-time as­sistance or support to peer supporters, as has been tested through the HAILEY (human-AI collaboration approach for empathy) chatbot (4), which was designed to enable more empathic conversations in text-based peer support.

In addition, although peer support is fundamentally grounded in lived experience, behavioral health encompass­es a vast number of different service users and diverse ex­periences: any single peer supporter will have direct knowl­edge of some, but not all, relevant areas of mental health and substance use. AI chatbots can be used to augment existing knowledge, such as through identification of resources (e.g., the hearing voices movement and its approach to voices for a peer supporter who lacks a relevant background or personal experience) and interactive conversations.

In many settings, peer supporters provide substantial re­source and systems navigation in addition to more social and emotional support. For example, peer supporters working in drop-in centers may help people locate emer­gency shelters; assist them with housing and disability benefit applications; and link them to pro bono legal sup­port, primary care, and dental services. Resource identifi­cation and navigation have been improved through the use of internet search engines, and today AI chatbots can even more quickly and effectively identify and enhance the nav­igation of available resources (5, 6).

In many settings, peer supporters provide substantial resource and systems navigation in addition to more social and emotional support.

Peer supporters may also be asked to support service users with technical needs, including those who have questions related to the criminal legal system, benefits eligibility, evic­tion, and medical issues (e.g., prevention and general medical health). Specialized legal (7, 8) and medical chatbots (or tools with chatbot functionality) may enable peer supporters to address or ameliorate a greater range of needs with greater sophistication, in turn strengthening their advocacy roles and capacity and contributing to the further democratization of knowledge in ways that align with the early goals of the peer support movement.

Another key benefit of AI is administrative support. AI tech­nologies can automate routine and time-consuming tasks, such as documentation, scheduling, and workflow coordina­tion, freeing peer supporters to focus on the relational aspects of their work. AI systems can significantly reduce adminis­trative burden and documentation time, which in turn may alleviate stress and enhance job satisfaction (9). Among peer support leadership, AI’s capacity for data analysis and trend identification can provide insights that enhance program de­cision making; for example, AI may assist leaders with identi­fying patterns in engagement, monitoring service use trends, and flagging disparities.

Despite the growing interest in applying AI, risks and cautions must be noted. Widespread availability of AI-driven support tools could undermine peer supporters’ skills and drive tech­nological dependence (i.e., a diminished capacity to respond to needs or problems without use of AI). AI-driven peer support assistance may also reinforce existing technocratic hierarchies and undermine the core focus on shared experience and rela­tionships as the primary nexus of support and healing.

Despite the growing interest in applying AI, risks and cautions must be noted. Widespread availability of AI-driven support tools could undermine peer supporters’ skills and drive technological dependence

AI hallucinations are a well-established problem, as are other forms of AI-driven misinformation and bias. Although the use of AI tools by peer supporters rather than directly by service users may insulate the latter from some of the biggest direct risks of chatbot interaction (e.g., chatbots that encourage suicide or exacerbate depression, anxiety, and anger toward others) (10), peer supporters could still encounter hallucinated resources or other difficulties related to technical assistance.

At present, AI training can only partially mitigate the risks of sociopolitical bias in AI technologies, potentially leading to information and guidance that are misaligned with the established values and practices of peer support. AI systems often lack the contextual judgment and ethical accountability that human peer supporters bring to crisis assessment and intervention. Because AI does not possess the capacity to make ethical judgment calls or to dynamically adapt in emergency situations, overreliance on AI in these contexts can increase risk for harm.

Because AI does not possess the capacity to make ethical judgment calls or to dynamically adapt in emergency situations, overreliance on AI in these contexts can increase risk for harm.

Concerns related to ethics and privacy are salient because AI systems often collect and process extensive amounts of personal data. This gap raises serious concerns about who has access to sensitive disclosures, the circumstances under which access is granted, and how the data may be reused or exposed. A recent scoping review identified privacy and confidentiality concerns in a majority of the articles that were examined, indicating pervasive uncertainty about data handling practices and their implications for user trust (11).

AI systems may operate with limited transparency and accountability. The black box nature of many machinelearning models means that understanding or contesting AI outputs is difficult for service users and peer supporters. This opacity may undermine trust, particularly among individuals with prior experiences of discrimination, marginalization, or trauma.

Despite frequent cost-savings arguments in favor of AI, implementation of AI-driven tools that are compliant with federal security and privacy laws (e.g., HIPAA or the Health Information Technology for Economic and Clinical Health Act) involves substantial costs. These costs include the integration of information technology (IT) systems with existing electronic health record (EHR) and care management platforms; ongoing maintenance and updates; cybersecurity infrastructure to protect sensitive behavioral health information; staff training and workflow redesign; and per-user fees, which are often substantial. Without regulation, AI vendors may use predatory pricing strategies, offering artificially low initial fees to secure adoption and then increasing costs once a provider entity has grown dependent on the technology and faces prohibitive switching costs. This lock-in dynamic is well documented in health care IT more broadly and poses risks to underresourced public behavioral health systems with limited negotiating power, especially peer-run organizations.

Nearly all currently available AI tools are owned by or depend on proprietary, commercial large language models (such as those developed and owned by OpenAI, Anthropic, Google, and Meta) that lack any direct accountability to the taxpayers who fund public behavioral health services. This arrangement risks a major shift of public dollars into the private sector and a transfer of control over clinical and administrative processes to technology companies that have no statutory obligations to the populations that clinics serve. Unlike traditional contracted services, where public entities retain some leverage through procurement processes, AI dependencies could create technical infrastructures that become impossible to extricate without major disruption. Moreover, unlike prior health IT (EHR systems, billing and case management software, etc.), GenAI does not function only as a data management and communications tool; it also has the potential to contribute substantial subject matter expertise and fundamentally shape treatment planning, day-to-day interactions, and therapeutic processes.

In addition, the inherent adaptation and unpredictability of AI means that integration differs markedly from past efforts to use technology to implement evidence-based or best practices as laid out in manuals, guidelines, and practice algorithms.

In addition, the inherent adaptation and unpredictability of AI means that integration differs markedly from past efforts to use technology to implement evidence-based or best practices as laid out in manuals, guidelines, and practice algorithms. GenAI’s potential degree of influence on the core clinical or practice work of peer support (and mental health services more broadly) should not be conflated with other IT and poses risks that clinic leaders must be vigilant in thinking through before naively pursuing adoption. From the perspective of the peer support movement, the risk for a commercial, proprietary takeover of what was originally a grassroots approach grounded in mutuality and bottomup empowerment represents a threat to peer supporters’ professional values.

Thus far, we have outlined considerations for leaders and peer support programs that are thinking about integrating AI into peer support. In the paragraphs that follow, we offer suggestions to help guide these decisions.

AI integration should emphasize training, human oversight, and ongoing evaluation. Training should focus not only on technical proficiency but also on ethical considerations, alignment with peer support values, and decision making in relational and crisis contexts. Organizational policies should clearly define AI’s supportive role, outline procedures in the event of crisis escalation following AI feedback, and ensure adherence to privacy standards. By embedding AI use within the framework of peer support values, peer leaders can ensure that technology enhances efficiency, expands reach, and supports peer staff without undermining mutuality, empathy, or trust, which are central to effective peer interventions (11, 12).

AI integration should emphasize training, human oversight, and ongoing evaluation.

People who receive peer support must retain choice in and control over AI use. Transparency about when AI is deployed, which data are collected, and how outputs inform support decisions is essential. Providing service users with the choice of opting out ensures that AI supplements rather than replaces the human connection, preserving the relational core of peer support. Maintaining this clarity reinforces autonomy, strengthens trust, and ensures that participants feel empowered in their own wellness journeys (11).

Transparency about when AI is deployed, which data are collected, and how outputs inform support decisions is essential.

AI should augment—not replace—the relational work of peer support. Ideally, AI will primarily assist with navigational and administrative tasks while peer supporters retain both control over and responsibility for building and sustaining human-to-human relationships rooted in trust, empathy, and mutuality. Training and implementation approaches should ensure AI literacy among peer supporters and other staff that encompasses comprehensive coverage of the risks and limitations of AI tools (including AI-generated hallucinations, misinformation, and bias as well as de-skilling and technological dependence) in peer support contexts.

In addition to HIPAA compliance, responsible implementation should provide transparency to service users, including explicit informed consent wherever possible, as well as contractual agreements with AI vendors that include prohibitions against using service user data for model training, accessible mechanisms for data review or deletion, and strong data security safeguards. Local system administrators must have sufficient expertise in AI and AI tool integration to ensure that privacy and confidentiality protections are maintained in the context of (local) implementation.

The behavioral health community in general, and peer leaders in particular, should consider public advocacy for regulatory frameworks designed to ensure that AI deployments in publicly funded systems remain accountable to taxpayers and service users, potentially including price controls, algorithmic transparency, data sovereignty protections, and public investment in fully open-source alternatives. The focus should be on developing and enforcing standards and safeguards that protect the relationship between a peer supporter and a service user. Policies should emphasize human oversight, informed consent, transparency, privacy, and data security. Clear legal and ethical frameworks would protect both service users and peer supporters from potential misuse while ensuring that AI tools enhance, rather than erode, trustbased relationships (12).

AI has the potential to strengthen peer support services if stakeholders—from funders to program leaders—prioritize human-centered designs, relational safeguards, and alignment with peer support values. Thoughtful policies, ethical oversight, and adequate training can ensure that AI augments the work of peer supporters and does not erode the trust and empowerment that are foundational to peer-led support.

AI also has the potential to augment peer support by increasing reach, efficiency, and access to information, but it must be deployed in ways that respect and preserve the human qualities that define peer support. Trust, empathy, shared experience, and relational connection cannot be replaced by AI. With thoughtful guidelines, training, governance, and ethical safeguards, AI can support but will never be a substitute for the human heart of peer work.

Trust, empathy, shared experience, and relational connection cannot be replaced by AI.