Expanding Internal Family Systems Through AI

Combining Internal Family Systems (IFS) therapy with AI-generated art unlocks powerful new pathways for emotional healing and self-discovery. AI art tools lower the barriers to creative expression, allowing individuals to quickly visualize and connect deeply with their inner parts, enhancing self-awareness and compassion. As an artist and current art therapy graduate student, I have experienced firsthand how combining AI-generated art with the IFS model can deepen relationships with our inner selves, expanding opportunities for meaningful personal growth.

IFS offers a compassionate, non-pathologizing approach to understanding the complex and often conflicting parts within us (Schwartz & Sweezy, 2019). When I first discovered IFS, it felt as if a light had suddenly switched on inside my mind—an experience I’m sure many can relate to. Integrating art therapy into my IFS practice felt like a natural progression, as I have always turned to visual art for introspection and self-discovery. Research highlights how art therapy complements the IFS framework by creating nonverbal avenues to externalize inner dynamics (Sabados, 2024).  This combination has helped me connect with my parts on a deeper level. Painting portraits of my parts helped me feel closer to them, and the process became cathartic. 

This creative endeavor was an absolute joy as someone who spends every precious free moment painting. Yet- not everyone feels comfortable engaging in traditional art-making. Many hesitate because of self-doubt, fears of embarrassment, or vulnerability tied to their artistic skills.

The introduction of AI-driven tools changes everything. By removing technical barriers, AI empowers people to visually represent their inner experiences, regardless of their artistic training or talent. While AI doesn’t replace traditional IFS work or therapeutic art-making, it greatly expands creative possibilities, inviting more individuals to connect visually with their internal parts.

When I began creating AI art using platforms like ChatGPT and DALL·E, the first figure I chose to represent was not exactly a part—but a guide. Aurora appears to me as the future self I aspire toward—calm, wise, grounded, and embodying clarity. She doesn’t carry burdens or ask for healing—she offers it. Guides, in contrast to parts, are supportive inner presences that emerge with insight, encouragement, and a sense of spiritual connectedness. They show up not to be helped, but to help (Schwartz & Newes, 2024). This mirrors my experience of Aurora, whose presence carries a deep sense of knowing and familiarity—as if she’s always been there, waiting patiently for me to recognize her.

Initially, I painted a traditional portrait of Aurora, a rewarding yet painstaking process that took months of careful adjustments to match the image in my mind. Introducing AI into my artistic toolkit offered a quicker, more fluid alternative—allowing me to create and refine representations in minutes. This streamlined method simplified the process and made it far less intimidating, especially for those unfamiliar with traditional artistic techniques. Preliminary research has confirmed that incorporating generative AI into creative processes can enhance opportunities for personal expression and reduce frustration (Schmutz et al., 2024). 

Below are several of the portraits I created with the help of AI. 

To fully appreciate their details and emotional depth, I invite you to zoom in:

Excited by the outcomes of my portraits, I expanded the process further to explore the relationships between my parts and Self. This led to creating a Parts Compass, a visual framework for mapping these dynamics. This tool helped me examine alliances, conflicts, and protective roles. 

The potential for integrating AI into therapeutic art-making and IFS therapy is immense. AI-generated visuals can enrich the unburdening process by creating tangible representations of exiled parts and their emotional burdens. Such visualizations could help clients witness, process, and release painful emotions. Clients could also utilize AI to create IFS family portraits, much like the Parts Compass, visually mapping the unique dynamics and relationships between their internal parts. These family portraits could offer clients greater clarity and perspective, illuminating the roles, alliances, and conflicts within their inner system. Beyond visual assistance, AI-driven prompts can also help clients articulate the emotions, needs, and stories connected to their internal parts.

Taking this further, AI can serve as a practical tool to deepen therapeutic insight and track clients’ progress over time. Therapists and clients could leverage AI to document and observe how burdened parts evolve into healthier, integrated states, providing new layers of clarity and understanding within IFS therapy. Moreover, AI integration could transform the way clients engage in therapy between sessions through personalized therapeutic assignments or “homework.” For example, AI might regularly prompt clients to reflect or participate in simulated dialogues with specific internal parts, guided by insights collaboratively provided by the therapist and client.

Expanding beyond the scope of IFS, AI also offers promising solutions to broader systemic challenges in the mental health field. Clinician burnout is rising alongside an increasing demand for mental health services, making innovative solutions essential (O’Connor et al., 2018). AI applications can alleviate some of these pressures by streamlining administrative tasks, supporting symptom monitoring, and facilitating structured interventions (Fitzpatrick et al., 2017).

AI also holds great promise for increasing global accessibility to therapeutic tools. The projected rise in cell phone users—from 7.1 billion in 2021 to an estimated 7.49 billion in 2025 (Taylor, 2023)—presents significant opportunities for integrating AI-driven mental health tools into everyday life. Recent research supports this, finding that tools like ChatGPT notably improved patient-reported quality of life in psychiatric inpatient settings, highlighting AI’s potential to enrich the client experience and complement clinical care meaningfully (Melo et al., 2024).

However, alongside these expansive possibilities, it is critical to consider the ethical implications of integrating AI into therapeutic contexts. At its current stage, AI is a valuable tool that supports but cannot replace the therapeutic relationship itself. This relational aspect is vital, as it forms the cornerstone of effective mental health treatment (Brown & Halpern, 2021). Key ethical concerns include protecting client privacy, preventing potential misdiagnoses, and ensuring culturally sensitive practices (Alfano et al., 2024). To responsibly advance AI-driven therapy, we must develop and rigorously test AI systems specifically designed for therapeutic purposes. These systems should adhere strictly to standards such as HIPAA to safeguard client information, mitigate cultural biases, and promote inclusivity in mental health care (Inkster et al., 2018).

AI-generated imagery offers a transformative new dimension to IFS therapy, opening doors to greater engagement, accessibility, and inclusivity. As we harness AI’s potential to deepen self-awareness and foster meaningful connections with our internal parts, we are invited into unexplored realms of creativity, challenging us to imagine therapeutic possibilities previously beyond reach. Realizing this immense promise demands thoughtful and ethical development—privacy protections, culturally responsive design, and trauma-informed frameworks must guide the integration of AI into therapeutic practices. Through careful collaboration among AI developers, IFS practitioners, and expressive therapists, this innovative technology can reshape therapeutic engagement, broaden accessibility, and significantly enhance the mental health landscape. Ultimately, the only true limitations are those within our own minds; our openness and imagination will define the future of AI’s role in therapeutic growth and creative self-expression.

References

Alfano, L., Malcotti, I., & Ciliberti, R. (2024). Psychotherapy, artificial intelligence and adolescents: ethical aspects. Journal of Preventive Medicine and Hygiene, 64(4), E438–E442. https://doi.org/10.15167/2421-4248/jpmh2023.64.4.3135

Brown, J. E. H., & Halpern, J. (2021). AI chatbots cannot replace human interactions in the pursuit of more inclusive mental healthcare. SSM Mental Health, 1, Article 100017. https://doi.org/10.1016/j.ssmmh.2021.100017

Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785

Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR MHealth and UHealth, 6(11), e12106. https://doi.org/10.2196/12106

Melo, A., Silva, I., & Lopes, J. (2024). ChatGPT: A pilot study on a promising tool for mental health support in psychiatric inpatient care. International Journal of Psychiatric Trainees, 2(2). https://doi.org/10.55922/001c.92367

O’Connor, K., Muller Neff, D., & Pitman, S. (2018). Burnout in mental health professionals: A meta-analysis. European Psychiatry, 53, 74–99. https://doi.org/10.1016/j.eurpsy.2018.06.003

Sabados, D. (2024). A Path Toward Healing: Integrating Internal Family Systems and Art Therapy. Art Therapy, 41(4), 194–202. https://doi.org/10.1080/07421656.2023.2292902

Schwartz, R. C., & Newes, S. (2024). Living Medicine Podcast: IFS, Psychedelics, and the Spirit World [Audio podcast]. Living Medicine. https://www.youtube.com/watch?v=tShUTEryvts

Schwartz, R. C., & Sweezy, M. (2019). Internal family systems therapy (2nd ed.). The Guilford Press.

Schmutz, Y. V., Kravchenko, T., Ben Souissi, S., & Kurpicz-Briki, M. (2024). Integrating generative AI into art therapy: A technical showcase. arXiv preprint arXiv:2412.03287. https://doi.org/10.48550/arXiv.2412.03287

Taylor, P. (2023, November 16). Forecast number of mobile users worldwide 2020–2025. Statista. https://www.statista.com/statistics/218984/number-of-global-mobile-users-since-2010/