Vanessa Farsadaki1,2,3*
Charla Griffy-Brown1- 1Thunderbird School of Global Management, Arizona State University, Phoenix, AZ, United States
- 2Space Exploration Strategies, Houston, TX, United States
- 3Kepler Space University, Bradenton, FL, United States
The integration of artificial intelligence (AI) and affective computing into behavioral health is transforming how mental wellbeing is assessed, monitored, and treated. As emotional and cognitive states can now be inferred through facial expressions, vocal tone, physiological signals, and behavioral cues, new technological paradigms are emerging to complement traditional approaches to mental healthcare. This is especially relevant in the wake of rising global mental health challenges, where access, personalization, and real-time feedback are essential components of effective care. Affective computing, a multidisciplinary field at the intersection of psychology, computer science, and cognitive science, seeks to enable machines to recognize, interpret, and respond to human emotions. When coupled with AI-driven data analytics and virtual reality (VR), it offers powerful tools for enhancing self-awareness, supporting clinical diagnostics, and delivering immersive therapeutic interventions. This paper explores how AI and affective computing can be leveraged across the behavioral health spectrum, from early detection and remote monitoring to therapy delivery and outcome prediction, with a particular emphasis on virtual environments as mediators of emotionally adaptive systems. We aim to review current innovations, examine their psychological validity, discuss ethical implications, and propose a research framework for advancing human-centered AI in behavioral health. Through this lens, we highlight the potential of emotionally intelligent systems not only to augment clinical practice but also to empower users in managing their mental wellbeing in real time.
1 Introduction
Behavioral health, encompassing mental health, emotional wellbeing, and substance use, remains one of the most complex and urgent domains in global healthcare (World Health Organization, 2022). Today, nearly one in every eight people worldwide lives with a mental health disorder, yet the majority do not receive adequate care (Ritchie and Roser, 2018). Access remains fragmented and uneven, stigma persists despite increasing awareness, and the cost of effective treatment continues to rise (Patel et al., 2018). The traditional boundaries of therapy, diagnosis, and support are being tested by a landscape marked by chronic provider shortages, economic strain, and social isolation, a reality brought into stark relief by recent global crises such as the COVID-19 pandemic (Moreno et al., 2020).
In this climate, the need for innovation in behavioral health is not just timely; it is imperative. Artificial intelligence (AI) and affective computing are transformative forces at the intersection of psychology, technology, and data science (Picard, 1997; Calvo et al., 2015). AI enables machines to learn from massive datasets, detect subtle patterns, and make predictions at scale (Esteva et al., 2019). Affective computing brings a critical human dimension: the capacity for machines to recognize, interpret, and respond to emotional states through cues such as facial expression, vocal tone, physiological responses, and behavior (Dzedzickis et al., 2020).
This technological synergy marks a profound shift in how we think about mental wellbeing and intervention. Traditional care models often rely on self-reporting and scheduled encounters, while AI and affective computing offer the promise of real-time, continuous insights. These systems can detect emotional changes before they escalate, personalize interventions, and reach people where and when they need it most (Torous et al., 2021). These technologies do not just augment the clinical toolkit; they have the potential to democratize behavioral health, breaking through barriers of access, stigma, and resource constraints (Kumar et al., 2022).
Further amplifying this transformation is the integration of virtual reality (VR) and immersive digital environments, which can mediate emotionally adaptive systems in entirely new ways (Freeman et al., 2017). Imagine a therapeutic platform that recognizes your emotional state and adapts in real time, offering tailored support, feedback, and immersive simulations that foster self-awareness and resilience (Fitzpatrick et al., 2017). By fusing AI, VR, and affective computing, we are moving closer to a model of behavioral health that is both highly personalized and universally accessible (Lindner et al., 2019).
The urgency is clear: global behavioral health cannot wait for incremental change. The fusion of AI and affective computing is not just a technological leap; it is a moral and practical imperative to build a future where mental wellbeing is within reach for all, powered by emotionally intelligent systems that understand, adapt, and empower (Topol, 2019).
2 Foundations of affective computing
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human emotions. It draws on psychology, computer science, and cognitive neuroscience to help machines interact with people in ways that feel more natural and empathetic (Picard, 1997; Calvo et al., 2015). Core concepts include emotion detection, sentiment analysis, and multimodal sensing, which together form the foundation of emotionally intelligent artificial intelligence (AI) (Poria et al., 2017).
Emotion detection involves identifying human feelings through observable cues such as facial expressions, vocal tone, and physiological changes (Soleymani et al., 2017). Sentiment analysis, often applied to text, uses natural language processing (NLP) to determine the emotional tone and intent behind words (Medhat et al., 2014). Multimodal sensing combines visual, auditory, and biometric data to form a more complete picture of a person's emotional state, with the goal of approaching the nuance of human perception (Zeng et al., 2009).
Examples of affective computing technologies include facial expression recognition systems that detect micro-expressions and subtle facial muscle movements, NLP algorithms that identify emotional content in speech and text, voice analytics tools that analyze pitch, tone, and prosody, and wearable biosensors that monitor signals such as heart rate, skin conductance, and respiration (Picard, 1997; Calvo et al., 2015). When used together, these modalities can generate real-time emotional profiles with a high degree of detail.
Despite advances, the field faces significant challenges. Many algorithms are trained on datasets lacking demographic diversity, which can lead to biased results and reduced accuracy for certain groups (Buolamwini and Gebru, 2018). Systems that perform well in laboratory conditions often struggle in real-world environments, where uncontrolled variables make prediction harder (McDuff et al., 2019). Cultural differences in emotional expression can also cause misinterpretation (Jack et al., 2012). To ensure affective computing technologies are both accurate and ethical, ongoing efforts must focus on reducing bias, improving inclusivity, increasing transparency, and designing systems that work across diverse contexts (Calvo et al., 2015; McDuff et al., 2019).
3 Applications in behavioral health
3.1 Early detection and monitoring
Artificial intelligence and affective computing have dramatically expanded our capacity for early detection and monitoring of behavioral health conditions. Passive data collection—gathering information from everyday device use without user intervention—has emerged as a powerful strategy for identifying early signs of depression, anxiety, and post-traumatic stress disorder (PTSD) (Torous et al., 2021). For example, algorithms can analyze patterns in smartphone sensor data, such as sleep disruptions, changes in social interaction, or reduced mobility, which often precede the onset or worsening of mental health symptoms.
Beyond sensors, AI chatbots now engage users in natural conversation to unobtrusively assess mood, thought patterns, and risk factors. Woebot, for instance, is a digital mental health agent that monitors user input for signs of depression or anxiety, providing support and escalating care if risk is detected (Fitzpatrick et al., 2017). Passive mobile sensing platforms like Mindstrong Health utilize smartphone usage data and keystroke dynamics to detect cognitive changes in real time, enabling clinicians to monitor patients remotely and intervene early (Onnela and Rauch, 2016).
3.2 Therapeutic interventions
The convergence of AI and virtual reality (VR) is redefining the therapeutic landscape. Emotionally adaptive VR environments, powered by affective computing, can sense a user's emotional state through physiological sensors or facial analysis and adjust therapeutic content accordingly (Freeman et al., 2017). For example, in exposure therapy for anxiety or PTSD, the virtual environment can be modified in real time to gradually introduce stressors or provide calming stimuli, maximizing both safety and efficacy (Lindner et al., 2019).
AI-enhanced cognitive behavioral therapy (CBT) extends beyond scripted interventions. Advanced systems, such as Wysa or Tess, use machine learning to deliver CBT in a conversational format, dynamically adapting guidance to user responses (Inkster et al., 2018). In addition, VR-based therapies increasingly incorporate biofeedback—providing users with real-time physiological information such as heart rate variability or skin conductance—to facilitate emotional regulation and accelerate therapeutic progress (Repetto et al., 2019).
3.3 Personalized behavioral coaching
Personalized behavioral coaching represents the next frontier in digital mental health. AI companions, designed to offer real-time emotional support, leverage advances in natural language processing and sentiment analysis to detect user needs and respond empathetically (Laranjo et al., 2018). These companions provide ongoing encouragement, suggest coping strategies, and can even escalate to human intervention when necessary.
Reinforcement learning allows these systems to refine their support over time, learning which interventions are most effective for each individual and adjusting recommendations dynamically (Hochreiter and Schmidhuber, 1997; Mohr et al., 2017). Integration with digital phenotyping—the continuous collection of behavioral data from personal devices—enables hyper-personalized interventions, matching support to each user's unique psychological profile and moment-to-moment context (Insel, 2017).
By combining affective computing, AI analytics, and immersive environments, these innovations are making behavioral health more proactive, personalized, and accessible than ever before.
4 Virtual environments as emotionally adaptive systems
4.1 The role of VR and immersive technology
Virtual reality (VR) and related immersive technologies are transforming the way behavioral health professionals deliver care, offering unprecedented opportunities to assess, treat, and support individuals in emotionally responsive ways. By creating fully controlled, interactive digital environments, VR allows clinicians to simulate real-world scenarios and observe user responses in a safe, repeatable manner (Freeman et al., 2017). This capability is particularly valuable for exposure therapy, where gradual, controlled exposure to feared stimuli can be tailored to each individual's needs and progress (Maples-Keller et al., 2017).
Moreover, VR's immersive qualities facilitate deep engagement, offering an experience that is not only multisensory but also emotionally evocative (Parsons and Rizzo, 2008). This makes VR an ideal platform for behavioral health interventions that require both active participation and emotional processing.
4.2 Personalization and user engagement
Affective computing further amplifies the impact of VR by enabling environments and avatars to detect and respond to users' emotional states in real time. Emotionally adaptive systems can monitor physiological data (such as heart rate or galvanic skin response), vocal tone, and facial expressions to personalize content, pacing, and difficulty level (Dzedzickis et al., 2020; Wiederhold and Riva, 2019). For example, a VR environment designed for social anxiety might scale the complexity of a virtual crowd based on the user's measured stress levels, providing optimal exposure while preventing overwhelm (Lindner et al., 2019).
Personalization is key to engagement and therapeutic effectiveness. Studies show that adaptive VR environments, which adjust in response to a user's affective signals, are perceived as more supportive and result in higher adherence to therapeutic protocols (Riva et al., 2019).
4.3 Case studies and real-world examples
4.3.1 Exposure therapy
One of the most validated uses of VR in behavioral health is for exposure therapy in anxiety and PTSD. In a clinical trial, VR-based exposure therapy demonstrated effectiveness for veterans with PTSD, providing controlled and repeatable traumatic scene re-creations while allowing real-time monitoring of user distress (Maples-Keller et al., 2017; Rizzo et al., 2015). The emotional adaptability of these environments enables clinicians to titrate exposure with unprecedented precision.
4.3.2 Stress reduction and resilience training
Immersive VR programs are also being used for stress reduction and resilience building. For example, VR mindfulness and relaxation environments, which respond to physiological feedback, have shown significant reductions in self-reported stress and physiological arousal among users (Annerstedt et al., 2013; Wiederhold et al., 2020). These systems often leverage soothing nature scenes or guided meditations that adjust according to real-time biometric data.
4.3.3 Emotionally responsive avatars and environments
Avatars in VR can be designed to mirror and respond to a user's facial expressions, posture, and tone of voice, enhancing feelings of social presence and empathy (de Melo et al., 2019). Such emotionally responsive avatars have been used to facilitate social skills training in individuals with autism and to provide supportive coaching for those with depression or anxiety (Georgescu et al., 2014).
4.3.4 VR-based emotion elicitation for clinical assessment
VR is increasingly recognized as a powerful tool for emotion elicitation and assessment in research and clinical settings. By placing individuals in controlled, immersive scenarios, clinicians can observe authentic emotional and behavioral reactions that might be difficult to replicate in traditional settings (Parsons and Rizzo, 2008; Shiban et al., 2015). This approach enables more nuanced assessment of emotional regulation, reactivity, and coping strategies.
Overall, VR and immersive technologies, when paired with affective computing, offer not only new modes of therapy but also rich opportunities for assessment, engagement, and empowerment in behavioral health.
5 Psychological validity and effectiveness
5.1 Evidence from recent studies
The psychological validity and clinical effectiveness of AI-driven affective computing and immersive technologies in behavioral health have been increasingly supported by a growing body of empirical research. Randomized controlled trials have demonstrated that AI-powered chatbots and virtual agents can reduce symptoms of depression and anxiety with effect sizes comparable to traditional low-intensity interventions (Fitzpatrick et al., 2017; Inkster et al., 2018). Similarly, VR-based exposure therapy has been shown to be as effective, if not superior, to in-person exposure therapy for a variety of anxiety disorders and PTSD (Carl et al., 2019; Maples-Keller et al., 2017).
Mobile sensing and digital phenotyping approaches, which leverage passive data from smartphones and wearables, have been validated as reliable tools for early detection and monitoring of mental health status. These technologies can predict clinical deterioration days or even weeks before conventional assessments, increasing opportunities for early intervention (Torous et al., 2021).
Furthermore, emotionally adaptive environments, where VR content or chatbot responses change dynamically in response to user affect, have been shown to improve engagement, reduce dropout rates, and enhance perceived support during therapy (Riva et al., 2019; Lindner et al., 2019). These findings highlight the promise of emotionally intelligent systems in not only delivering effective care but also in fostering therapeutic alliance and trust.
5.2 Strengths and limitations
Among the strengths of AI and affective computing in behavioral health is scalability. Digital interventions can be delivered across geographies, reducing barriers of access, cost, and stigma that have traditionally limited care (Kumar et al., 2022). These systems also support continuous, real-time monitoring, enabling truly personalized interventions and the capacity to reach high-risk individuals outside clinical settings (Insel, 2017).
However, limitations remain. Psychological validity is contingent on the quality and representativeness of training data; many AI systems risk perpetuating bias if not carefully designed and validated across diverse populations (Obermeyer et al., 2019). While digital tools can enhance engagement, they may not be suitable for everyone, individual differences in technology acceptance, digital literacy, and trust must be considered (Mohr et al., 2017). Additionally, some studies report that automated interventions are most effective when integrated with some form of human support or oversight, rather than being fully autonomous (Laranjo et al., 2018).
Another challenge is the potential for over-reliance on quantitative data, which may not fully capture the nuanced, contextual factors that influence mental health (Harari et al., 2016). Ethical concerns around privacy, data security, and informed consent also require robust regulatory frameworks and ongoing vigilance (Moreno et al., 2020).
5.3 Real-world outcomes
Deployment at scale shows that emotionally aware, AI-enabled tools can produce measurable improvements in symptoms and functioning across diverse populations. AI chatbots such as Woebot and Wysa report reductions in depressive and anxiety symptoms comparable to low-intensity psychological interventions, with strong engagement in real-world use (Fitzpatrick et al., 2017; Inkster et al., 2018). VR exposure programs have translated into clinically meaningful and durable gains for PTSD and anxiety in health system settings, including veteran care, where controlled stimulus delivery and repeatability matter (Rizzo et al., 2015; Maples-Keller et al., 2017; Carl et al., 2019). Digital phenotyping has enabled earlier detection of deterioration through continuous behavioral signals, which creates new windows for timely outreach and risk mitigation in daily life contexts, not only in clinic visits (Onnela and Rauch, 2016; Torous et al., 2021).
These outcomes are driven by three reinforcing mechanisms. First, reach and immediacy: 24/7 availability lowers access barriers and stigma while supporting just-in-time interventions. Second, personalization: affect-sensitive systems adapt content and pacing to the user's state, which strengthens alliance and adherence. Third, continuous observation: passive sensing and repeated measures detect small changes that accumulate into clinically relevant signals. Benefits are moderated by factors such as severity, digital literacy, and preference for human contact, and they depend on privacy, safety, and equity safeguards that sustain trust (Riva et al., 2019; Lindner et al., 2019; Price and Cohen, 2019; Obermeyer et al., 2019).
In practice, the strongest and safest gains appear when these tools are embedded in stepped-care pathways that include human oversight, clear escalation protocols, and transparent data practices. Real-world effectiveness therefore hinges on thoughtful integration into clinical workflows, ongoing monitoring of equity and bias, and participatory feedback that keeps systems aligned with user goals and values (Topol, 2019; Price and Cohen, 2019; Obermeyer et al., 2019).
6 Affective computing in behavioral health: ethical and practical challenges
Table 1 shows that integrating affective computing into behavioral health offers transformative potential, but it also brings significant ethical and practical challenges. Because these systems handle deeply sensitive emotional data and mental health indicators, privacy, consent, and algorithmic fairness must be central design priorities (Shen et al., 2020; Vinciarelli and Mohammadi, 2014).
Table 1. Real-world behavioral outcomes from AI and affective computing interventions.
Privacy is one of the most pressing concerns in emotional AI. Emotionally responsive systems often require continuous data collection from personal devices, wearable biometric sensors, and even social interactions (McDuff et al., 2019). Safeguarding this information involves ensuring informed consent, collecting only the minimum necessary data, and giving users control over how their information is stored and shared (Martínez-Miranda and Aldea, 2005). Behavioral health data is especially sensitive, and privacy-by-design approaches are essential. These should include strong encryption, secure storage systems, and compliance with frameworks such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union (Hao et al., 2021).
Bias and fairness in emotion recognition systems represent another serious challenge. Many current AI models are trained on datasets that fail to adequately represent different ages, races, cultural backgrounds, or genders (Buolamwini and Gebru, 2018). This lack of diversity can lead to misinterpretation of emotional signals or inappropriate system responses—an especially dangerous risk in mental health contexts where errors can affect diagnosis or care (Howard and Borenstein, 2018). Addressing this problem requires inclusive data collection, algorithmic transparency, and ongoing monitoring for disparate impacts (Shen et al., 2020).
Human-AI interaction and trust are equally critical for adoption in behavioral health. For these systems to be effective, users must view them as credible, empathetic, and respectful of their emotional states (Lisetti et al., 2013). Designing with explainability in mind—so users understand how and why the system responds in a particular way—can help foster a therapeutic alliance (Abd-Alrazaq et al., 2019). Without trust, even the most advanced AI tools will fail to achieve meaningful engagement.
Finally, ethical deployment requires interdisciplinary oversight. Clinicians, technologists, ethicists, and patients should collaborate to set standards for transparency, accountability, and consent (Hao et al., 2021). This collaboration not only mitigates risks but also ensures that affective computing in behavioral health aligns with human values and contributes positively to mental health outcomes (Howard and Borenstein, 2018).
7 A framework for human-centered AI in behavioral health
7.1 Principles of design and deployment
The preceding sections surveyed current affective computing and AI applications in behavioral health, identifying their strengths, limitations, and real-world outcomes. Building on this review, Section 7 proposes a human-centered deployment framework to ensure that emotionally intelligent behavioral health systems are safe, ethical, equitable, and clinically meaningful. The principles outlined here were selected based on their recurrence across major digital mental health guidelines (e.g., Topol, 2019; Calvo et al., 2015), WHO and APA standards for responsible digital care, and AI ethics frameworks emphasizing trust, transparency, and inclusion as prerequisites for therapeutic adoption and effectiveness.
Each principle reflects a critical requirement for behavioral health: safety and privacy protect users from harm; empathy and engagement ensure adherence; transparency and explainability foster trust; inclusivity and equity address biases and disparities; and empowerment and co-design sustain long-term user relevance. These principles are interdependent and collectively serve as guardrails for implementation. Their practical effectiveness depends on translating each principle into specific technical, clinical, and policy strategies that can be operationalized in real-world deployments. These human-centered principles, along with their ethical justification, enabling technologies, and policy alignment, are mapped in Table 2.
Table 2. Mapping human-centered principles to their justification, enabling technologies, policy alignment, and implementation pathways.
Operationalizing these principles requires cross-functional alignment among AI developers, behavioral clinicians, ethicists, policymakers, and end-users. For instance, an affective VR therapy platform designed under this framework would incorporate encryption (safety), adaptive avatar responsiveness (empathy), user-facing emotional feedback explanations (transparency), validated cross-cultural emotion models (equity), and iterative patient feedback loops (co-design). In this way, the framework provides both a conceptual foundation and a deployment roadmap toward responsible and effective affective AI in behavioral health.
8 Research gaps and future directions
Despite rapid advances, current applications of affective computing and AI in behavioral health face several unresolved gaps that limit their long-term efficacy, safety, scalability, and equitable adoption. These gaps were identified based on recurring limitations observed in existing systems (Sections 3–6), implementation challenges (Section 5.3), and unmet needs for human-centered alignment (Section 7.1). They fall into four main categories: technological challenges, clinical integration gaps, policy and regulatory limitations, and ethical/social considerations. Addressing these gaps is essential for moving from experimental success toward safe, effective, and sustainable real-world deployment at scale. Key research gaps across technology, clinical integration, policy, and ethics, along with their relative priority, are outlined in Table 3.
Table 3. Key research gaps, their significance, and priority levels for advancing affective computing and AI applications in behavioral health.
8.1 Technology gaps
8.1.1 Longitudinal validation remains limited
Most current evaluations are short-term, which restricts understanding of how emotional AI performs in prolonged use across mental health trajectories (Lindner et al., 2019).
8.1.2 Multimodal data fusion is still underdeveloped
Effective affective computing requires integrating voice, facial expression, behavior, and physiological biosignals, yet many current systems rely on single modalities, which limits emotional sensitivity and resilience in real-world conditions (Dzedzickis et al., 2020; Harari et al., 2016).
8.1.3 Context-aware emotional modeling is insufficient
Emotion recognition systems often lack situational awareness and fail to differentiate between clinically relevant emotional states and benign affective fluctuations, reducing diagnostic precision.
8.2 Clinical integration gaps
8.2.1 Limited interoperability with existing healthcare systems
AI-generated emotional insights are often siloed, lacking seamless integration into electronic health records and clinical workflows (Topol, 2019).
8.2.2 Inadequate human-AI hybrid care models
Fully automated mental health support may not be equally effective for all users; hybrid models that define when and how clinicians intervene remain underdeveloped (Mohr et al., 2017).
8.2.3 Unclear frameworks for clinical responsibility and escalation
There is still limited guidance on how clinical accountability should be managed when AI-driven emotional inferences are used in patient care.
8.3 Policy and regulatory gaps
8.3.1 Lack of standardized regulatory pathways for emotional AI
Most existing frameworks (e.g., GDPR, HIPAA) address data privacy but not affective inference risk or emotional manipulation.
8.3.2 No universal benchmarks for assessing accuracy and fairness in emotion recognition
Regulatory bodies lack validated performance thresholds for approving affective computing tools, especially those used in high-stakes behavioral health decision-making.
8.4 Ethical and societal gaps
8.4.1 Emotional surveillance concerns
Prolonged monitoring of affective signals raises questions about user autonomy, emotional manipulation, and consent boundaries (Shen et al., 2020).
8.4.2 Bias and cultural misinterpretation persist
Even improved datasets struggle to fully capture diverse affective norms, risking misclassification and inequitable treatment (Buolamwini and Gebru, 2018).
8.4.3 Digital exclusion risks
Populations with low digital literacy, limited connectivity, or mistrust in AI may be further marginalized if emotionally adaptive care becomes standard without inclusive strategies.
Addressing these gaps requires coordinated collaboration between developers, behavioral clinicians, ethicists, regulatory agencies, and affected communities. Future research must focus on building context-aware, culturally adaptive emotional models; validating long-term effectiveness in diverse populations; constructing clinically actionable AI-human collaboration protocols; and developing policy frameworks that ensure fairness, transparency, and accountability. Only through a multidisciplinary and inclusive approach can AI-driven affective computing evolve into a trusted pillar of behavioral healthcare.
9 Discussion
The integration of artificial intelligence and affective computing into behavioral health is redefining the landscape of mental wellbeing and care. These technologies, when thoughtfully designed and ethically deployed, have the power to dramatically improve behavioral health outcomes on a global scale. From early detection and real-time monitoring to adaptive therapeutic interventions and personalized coaching, AI-driven systems are enhancing access, personalization, and efficacy in ways that traditional approaches alone could never achieve (Fitzpatrick et al., 2017; Torous et al., 2021).
Virtual reality and immersive environments further amplify this potential, offering emotionally responsive systems that foster engagement, resilience, and deep personal insight (Freeman et al., 2017; Riva et al., 2019). As a result, the promise of emotionally intelligent, human-centered digital care is no longer a distant vision—it is already beginning to transform how we diagnose, treat, and support mental health for millions worldwide.
Yet, realizing the full potential of these innovations requires more than technological advancement. It demands genuine, sustained interdisciplinary collaboration. Clinicians, technologists, ethicists, designers, researchers, and patients themselves must work together to ensure that these tools are safe, effective, transparent, and equitable (Topol, 2019; Moreno et al., 2020). Only by bridging the worlds of clinical wisdom, ethical rigor, and human-centered design can we build emotionally intelligent systems that are truly fit for purpose.
The call to action is clear: we must break down silos and foster active partnerships across disciplines and sectors. Together, we can shape a future where behavioral health support is accessible, adaptive, and empowering for all—delivered not just by machines, but by emotionally aware, human-guided AI that honors the complexity and dignity of every individual.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
VF: Investigation, Writing – review & editing, Conceptualization, Writing – original draft. CG-B: Writing – original draft.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declare that no Gen AI was used in the creation of this manuscript.
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Keywords: affective computing, behavioral health, artificial intelligence, virtual reality (VR), digital phenotyping
Citation: Farsadaki V and Griffy-Brown C (2026) AI affective computing and behavioral health. Front. Comput. Sci. 7:1692728. doi: 10.3389/fcomp.2025.1692728
Received: 06 September 2025; Revised: 04 November 2025;
Accepted: 25 November 2025; Published: 14 January 2026.
Edited by:
Annahita Nezami, Kepler Space Institute, United StatesReviewed by:
Yen-Hung Hu, Norfolk State University, United StatesCopyright © 2026 Farsadaki and Griffy-Brown. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Vanessa Farsadaki, dmZhcnNhZGFraUBzZXN0cmF0ZWdpZXMub3Jn