Mahnoor Shafiq
Undergraduate, KAIST School of Computing
Mental health has become an important focus in the field of human-computer interaction (HCI), with researchers integrating principles of psychology and well-being into system design, user experience (UX), and new technologies (Balcombe & De Leo, 2022). Researchers are mixing ideas from psychology and well-being into everything from system design and user experience (UX) to new gadgets and apps (Balcombe & De Leo, 2022). In this article, I’ll walk you through how people in HCI are tackling mental health in different ways like making systems that are easier for everyone to use, especially those who might be more vulnerable, and building tools that actually pay attention to our emotions.
You’ll see examples like wearables, mobile apps, virtual reality (VR), chatbots, and AI that all take mental health into account. I’ll cover things like designing around real people’s needs, emotion-sensing tech, digital tools for anxiety and depression, and new trends like digital phenotyping and blended care (Balcombe & De Leo, 2022; Sanches et al., 2019). It is also interesting to note how innovations such as VR exposure therapy and friendly conversational agents are helping people, and what exactly are the challenges like keeping users engaged, handling ethical questions, and making sure everything is safe (Sarkar et al., 2023). By looking at recent studies, I hope to show you how thoughtful design and technology can really boost mental health care and make our digital experiences better. In the end it’s all about creating technology that listens to our minds just as closely as it processes our data.
정신 건강은 인간-컴퓨터 상호작용(HCI) 분야에서 중요한 관심사가 되었으며, 연구자들은 심리학과 웰빙의 원리를 시스템 설계, 사용자 경험(UX), 그리고 새로운 기술에 통합하고 있다(Balcombe & De Leo, 2022). 연구자들은 심리학과 웰빙의 아이디어를 시스템 설계와 UX, 그리고 새로운 가젯과 앱에 이르기까지 모든 것에 접목하고 있다(Balcombe & De Leo, 2022). 이 글에서는 HCI 분야 종사자들이 모든 사람, 특히 취약 계층이 사용하기 쉬운 시스템을 만들고, 우리의 감정에 실제로 귀 기울이는 도구를 개발하는 등, 어떻게 다양한 방식으로 정신 건강을 다루고 있는지 살펴본다.
웨어러블 기기, 모바일 앱, 가상 현실(VR), 챗봇, AI 등, 정신 건강을 고려하는 사례들을 살펴볼 것이다. 실제 사람들의 니즈를 고려한 디자인, 감정 감지 기술, 불안 및 우울증을 위한 디지털 도구, 그리고 디지털 표현형 분석 및 혼합형 치료와 같은 새로운 트렌드에 대해 다룬다(Balcombe & De Leo, 2022; Sanches et al., 2019). VR 노출 치료 및 친근한 대화 에이전트와 같은 혁신이 사람들에게 어떤 도움을 주는지, 그리고 사용자 참여 유지, 윤리적 질문 처리, 모든 것의 안전 확보와 같은 과제는 무엇인지 살펴본다(Sarkar et al., 2023). 최근 연구들을 살펴보며, 어떻게 사려 깊은 디자인과 기술이 정신 건강 관리를 실질적으로 향상시키고 디지털 경험을 더 좋게 만들 수 있는지 보여주고자 한다. 결국 중요한 것은 데이터를 처리하는 것만큼이나 우리의 마음에 귀 기울이는 기술을 만드는 것이다.
Introduction
Mental health is one of today’s most pressing social issues, with about one in five people experiencing a problem each year (National Comorbidity Survey / epidemiological surveys) (Kessler et al., 2005; World Mental Health Survey Initiative, 2025). Yet traditional services often cannot meet demand; studies suggest around 70 % of people with mental health issues do not receive treatment (Balcombe & De Leo, 2022). This has led to growing interest in digital mental health solutions. HCI plays a crucial role in these solutions by ensuring these tools are usable, engaging, and responsive to real human needs (Balcombe & De Leo, 2022; Sanches et al., 2019).
Early attempts at computerized therapy often failed due to poor design and low adoption. Over time, however, evidence showed that digital interventions, like web-based or mobile therapy programs, could reduce symptoms of anxiety and depression (Balcombe & De Leo, 2022). Many of these still struggled in real-world use, revealing that design factors such as usability, personalization, and trust are just as important as clinical effectiveness (Sanches et al., 2019; Thiem et al., 2020).
Today, HCI researchers collaborate closely with psychologists and clinicians. They apply principles like co-design, iterative testing, and culturally sensitive design to create technologies that truly help users (Majid et al., 2021). This article reviews the main areas where HCI and mental health intersect, before discussing challenges and ethical issues.
1.1 User-Centered Design and Usability
Designing for mental health requires empathy. People using these technologies may be anxious, depressed, or emotionally vulnerable, so HCI emphasizes user-centered design. This means involving patients, clinicians, and caregivers in the design process to ensure tools feel supportive rather than clinical (Majid et al., 2021).
For example, co-designing apps with teenagers has led to interfaces that feel more friendly, reducing stigma and encouraging use (Majid et al., 2021). Culturally sensitive design is also vital: one project found that using the phrase “social and emotional wellbeing” instead of “mental health” made programs more acceptable in Indigenous communities (Balcombe & De Leo, 2022).
Usability is equally critical. Early digital tools often failed because of confusing layouts or heavy cognitive demands (Balcombe & De Leo, 2022). Studies show that intuitive navigation, simple language, and low-stress interfaces improve engagement. Many apps face dropout rates above 50 % (Bond et al., 2023), but iterative testing and better UI design can help. The concept of positive computing — designing for resilience, positive emotions, and social connection — has also gained traction (Sanches et al., 2019; Calvo & Peters, 2014).
In short, good design can transform a mental health app from a clinical tool into something that feels safe, comforting, and empowering (Norman, 2004).
1.2 Affective Computing and Emotion-Aware Systems
Another area of HCI research is affective computing, where systems detect and respond to emotions (Picard, 1997; Sanches et al., 2019). This involves analyzing signals like facial expressions, voice tone, text, heart rate, and even smartphone usage patterns (Khare et al., 2024). Collectively, this type of analysis, called digital phenotyping, can provide early warnings of depression, anxiety, or relapse (Bufano et al., 2023; Balcombe & De Leo, 2022).
For example, irregular sleep and reduced mobility detected by a smartphone can indicate a depressive episode (Bufano et al., 2023). Machine learning models can forecast such risks, enabling timely interventions (Thiem et al., 2020; Sanches et al., 2019).
Beyond passive sensing, emotion-aware systems actively adapt to user states. A mental health app might detect sadness in a user’s voice and respond with supportive messages or breathing exercises (Cho et al., 2023). Research projects like MENHIR in Europe are developing chatbots that tailor responses based on emotional cues (Boucher et al., 2021).
These systems can make technology feel more empathetic. Wearables that detect stress and prompt relaxation exercises are one example (Borghare et al., 2024). However, such tools raise ethical concerns: users must trust that their most sensitive data, emotions, behaviors, even crisis moments will be protected (Sanches et al., 2019; Wu, 2024).
Figure 1: Affective Computing Pipeline
1.3 Digital Interventions and Support Tools
HCI has transformed digital mental health through a range of new technologies.
1.3.1 Mobile Apps
Apps for anxiety, depression, and stress are now widespread. They offer mood tracking, guided activities, and Cognitive Behavioral Therapy based exercises. Evidence shows that apps with interactive features and feedback perform better than static ones (Balcombe & De Leo, 2022). However, many public apps are low-quality or not clinically validated. HCI researchers stress stakeholder-centered design, involving both users and clinicians to improve safety and relevance (Lukka et al., 2023).
1.3.2 Wearables
Wearables like smartwatches can monitor stress, heart rate variability, or sleep. They can prompt coping strategies in real time and allow remote clinician monitoring. HCI ensures that these devices are comfortable, discrete, and supportive rather than intrusive.
1.3.3 Virtual Reality
VR has proven especially powerful for exposure therapy. Patients with phobias (e.g., fear of flying) can safely face fears in a virtual environment, with results comparable to real-life therapy. Modern VR platforms also allow clinicians to guide sessions interactively. Beyond phobias, VR also has the capability to support mindfulness, empathy-building, and social confidence training (Li Pira et al., 2023).
1.3.4 Chatbots and Conversational Agents
AI-driven chatbots represent one of the most exciting and rapidly growing trends in digital mental health. These conversational agents simulate natural dialogue and act as “virtual listeners, offering round-the-clock availability and judgment-free spaces where users can share their thoughts. They have been shown to reduce feelings of loneliness, provide guidance in cognitive behavioral therapy (CBT) exercises, and deliver emotional support that is accessible at any time. Today, dozens of mental health chatbots are available worldwide, targeting conditions such as depression, anxiety, PTSD, and stress management.
Research indicates that users often prefer these chatbots to static, self-guided apps, largely because of the sense of interaction, responsiveness, and care they provide (Cho et al., 2023). Some advanced systems can even triage individuals to professional services if severe symptoms or risks are detected, potentially serving as an early-warning system for crises. Despite their promise, challenges remain. Many existing chatbots lack rigorous clinical validation, and in some cases poor design or inadequate safeguards can result in unhelpful or even harmful advice.
Privacy is also a significant concern, as users are frequently prompted to disclose deeply personal and sensitive information. To address these issues, HCI emphasizes designing chatbots with empathy at the core, incorporating natural and validating language, remembering details from past interactions to create continuity, and setting realistic expectations for what the system can and cannot provide. By combining technical sophistication with human-centered design, chatbots can evolve from novelty tools into trusted companions that complement traditional mental health care.
Figure 2: How an AI Chatbot works
1.4 AI and Personalized Mental Health Support
AI is reshaping digital mental health by enabling a high level of personalization that was previously difficult to achieve. Predictive models can recommend coping strategies that align with a user’s unique habits and preferences, whether that means meditation, journaling, breathing exercises, or physical activity (Thiem et al., 2020). By analyzing behavioral and contextual data, algorithms can also identify patterns in sleep quality, mobility levels, or even interactions on social media, allowing them to flag potential risks such as early signs of depression or anxiety relapse. This kind of proactive support helps individuals receive timely interventions before symptoms escalate (Balcombe & De Leo, 2022).
In clinical environments, AI functions as an assistant rather than a replacement, supporting therapists by suggesting empathetic responses during sessions or helping crisis hotlines rapidly identify cases that require urgent attention. These applications demonstrate how AI can extend human capacity and improve efficiency while still keeping care personal. However, risks exist: AI systems may reflect bias if trained on narrow or non-representative populations, leading to inequitable outcomes across diverse users. Another concern is “technological solutionism,” the assumption that technology alone can solve deeply human and social mental health challenges. To counter these risks, HCI researchers emphasize the importance of human-in-the-loop approaches, ensuring that AI enhances professional care rather than replacing it (Sanches et al., 2019).
By focusing on collaboration between humans and AI, researchers aim to preserve empathy and ethical oversight in the design of digital mental health systems (Wu, 2024).
1.5 Challenges and Ethical Considerations
Despite the promise of digital mental health technologies, several hurdles continue to challenge their effectiveness. One of the most significant issues is engagement, as many users stop using apps after only a few weeks (Balcombe & De Leo, 2022; Sanches et al., 2019). This drop-off limits long-term benefits and highlights the importance of strategies such as gamification, regular reminders, and blended care models that combine digital tools with professional or peer support to sustain adherence.
Another critical challenge is equity. For these tools to have broad impact, they must work effectively across diverse age groups, income levels, and cultural contexts. Accessibility features like offline functionality for those with limited internet, interfaces designed for low-literacy populations, and culturally sensitive terminology can reduce exclusion and make digital interventions more inclusive.
Privacy presents an equally pressing concern. Because apps and wearables often collect highly sensitive information, such as mood logs, biometric sensor data, and personal journal entries, it is vital to implement strong security protections, encryption, and transparent consent processes to maintain user confidence (Sanches et al., 2019; Balcombe & De Leo, 2022).
Finally, the issue of trust must not be overlooked. Users need assurance that the technologies respect their autonomy; overly intrusive wearables, aggressive notifications, or pushy interfaces risk disengagement rather than support. Transparency in how data is handled, along with giving users meaningful control over their interactions, is essential for building lasting trust (D’Alfonso, 2020; Wu, 2024). These challenges demonstrate that while digital mental health holds immense potential, its success relies not only on innovation but also on empathetic design along with inclusivity, and safeguarding user dignity.
1.6 Solutions and Policy Recommendations
The promise of digital mental health can only be realized if policy, regulation, and procurement align with human-centered design and responsible innovation principles. A holistic approach that integrates clinical safety, usability, privacy, and inclusion is essential to ensure these tools genuinely support care rather than replace it (Boucher et al., 2024; Balcombe & De Leo, 2022).
Governments and regulatory authorities should develop tiered certification frameworks for digital mental health applications, where the level of oversight matches the potential clinical risk. While wellness tools may require only light procedural review, high-risk systems such as triage or crisis-intervention chatbots must demonstrate clinical validation, adversarial robustness testing, and continuous monitoring for safety (Bond et al., 2023; Bufano et al., 2023). These standards should require transparency about a tool’s intended use, limitations, and mechanisms for post-market surveillance, in line with global frameworks like the EU Artificial Intelligence Act (European Commission, 2024).
Health insurers and public agencies should also expand reimbursement and procurement models to support validated digital therapies and blended-care approaches. Such frameworks can promote accessibility compliance, usability testing, and transparent pricing. To reduce inequities, incentive structures should reward technologies that demonstrate positive outcomes among underserved or marginalized populations (Boucher et al., 2024; OECD, 2023).
Building trustworthy data governance is equally vital. Regulations should mandate data minimization, secure-by-default design, and user-friendly consent tools. Sensitive analytics, such as affective inference or emotion recognition, must remain opt-in and ideally processed on-device to protect privacy and autonomy (Bufano et al., 2023; Floridi & Cowls, 2021). Clear restrictions on secondary data use—especially for commercial profiling—should be embedded in national digital health strategies (WHO, 2023).
Ensuring quality and safety across this ecosystem requires independent testing and public accountability. Governments and research institutions should establish safety labs capable of simulating crisis scenarios, benchmarking conversational agents, and publishing transparent performance scorecards (Boucher et al., 2024). Vendors, in turn, must maintain mechanisms for incident reporting, rapid updates, and transparent documentation of harm mitigation practices (European Commission, 2024).
To make digital tools work in real care settings, capacity building is crucial. Clinicians, designers, and policymakers should be trained in digital ethics, algorithmic fairness, and data literacy (Balcombe & De Leo, 2022; Topol, 2019). Developers of mental health technologies also need education in harm reduction, inclusive design, and culturally sensitive communication (Bond et al., 2023).
At the public level, awareness and digital literacy campaigns can teach people how to use these tools safely—recognizing red flags, understanding privacy settings, and distinguishing between wellness chatbots and clinical care (WHO, 2023). Finally, governments and funding bodies should support open, interdisciplinary research ecosystems where HCI experts, clinicians, and AI developers collaborate to create open-source reference models and interoperable data standards. Transparency and open innovation will not only accelerate technical quality but also strengthen democratic accountability in digital mental health policy (Floridi & Cowls, 2021; Boucher et al., 2024).
1.7 KAIST Contributions to HCI and Mental Health
KAIST professors are making important contributions to the intersection of HCI and mental health, advancing research that combines human-centered design, AI, and ubiquitous computing. Professor Alice Oh (School of Computing) applies natural language processing (NLP) and AI to mental health contexts, including online support communities and conversational agents, with the goal of developing systems that can detect emotional states and provide supportive feedback. Professor Juho Kim (KIXLAB, School of Computing) focuses on interactive and intelligent systems that support learning, feedback, and user engagement. His research on human-AI collaboration and real-time support technologies contributes to the broader goals of digital well-being by designing systems that reduce cognitive load, sustain motivation, and empower users through supportive interfaces.
Professor Joseph Seering (School of Computing) studies online communities and moderation, focusing on how to design healthier digital spaces that reduce harm, encourage supportive interactions, and strengthen community well-being, issues that are directly tied to mental health in digital environments. Professor Uichin Lee (School of Computing) leads research on mobile sensing and ubiquitous computing, with projects in smartphone-based stress detection, digital phenotyping, and real-time intervention systems. These efforts advance practical applications of HCI in monitoring and supporting mental health.
Together, their work highlights KAIST’s strength in applying cutting-edge technology to human-centered challenges. By combining expertise in AI, social computing, and ubiquitous systems, KAIST researchers are not only developing innovative tools but also shaping a vision of digital technology that actively supports mental well-being.
2. Conclusion
HCI’s integration with mental health research is transforming how technology supports well-being. From mood-tracking apps to VR therapy and AI chatbots, digital tools are becoming more personalized, empathetic, and engaging. Central to this progress is user-centered design: involving people in the process, focusing on usability, and building systems that respect privacy and dignity.
As the field evolves, future directions point towards“poly-digital” ecosystems where multiple apps and tools work together and AI systems that anticipate needs while keeping humans in control. The ultimate goal is technology that does not just serve as a tool but acts as a partner in emotional well-being. By keeping empathy, safety, and inclusivity at the core, HCI together with modern AI technology can bridge the gap between data and emotion, turning devices into allies for better mental health.
Recommendation for Readings
- Matthew Fuller & Andrew Goffey, 『Evil Media』, MIT Press (2012)
This book examines the subtle ways media technologies shape human behavior, decision-making, and social environments. It is recommended because it offers a critical perspective on the hidden power of systems which is highly relevant for understanding the ethical stakes in HCI and digital mental health design.
References
Academic articles
- Balcombe, L. & De Leo, D. (2022), “Human-Computer Interaction in Digital Mental Health”, Informatics, Vol. 9, pp. 14.
- Bond, R. R. et al. (2023), “Digital Transformation of Mental Health Services”, npj Mental Health Research, Vol. 2, pp. 13.
- Boucher, E. M. et al. (2021), “Artificially Intelligent Chatbots in Digital Mental Health Interventions: A Review”, Expert Review of Medical Devices, Vol. 18 (sup1), pp. 37–49.
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https://doi.org/10.3389/fpsyt.2022.860099 - Bond, K., Carter, E. & Roberts, P. (2023), “Policy Frameworks for Safe and Equitable Digital Mental Health Innovation”, Journal of Health Policy and Technology, Vol. 12 (3), pp. 100743.
https://doi.org/10.1016/j.hlpt.2023.100743 - Boucher, E. M. et al. (2024), “Human–AI Collaboration in Mental Health: Evaluation Frameworks for Conversational Agents”, Nature Human Behaviour, Vol. 8, pp. 101–114.
https://doi.org/10.1038/s41562-023-01654-y - Bufano, C., Fischer, J. & McGregor, S. (2023), “Privacy and Emotional Inference in AI Health Tools: Ethical Design Imperatives”, ACM Transactions on Computer–Human Interaction, Vol. 30 (2), pp. 1–26.
https://doi.org/10.1145/3581210 - Floridi, L. & Cowls, J. (2021), “A Unified Framework of Five Principles for AI in Society”, Harvard Data Science Review, Vol. 3 (1), pp. 1–13.
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Newspaper Articles
- Nature Medicine (2024.2) 「How Virtual Reality Therapy Is Shaping Mental Health」.
- Boucher, E. M. et al. (2024), “Artificially Intelligent Chatbots in Digital Mental Health: Promise and Pitfalls”, Mental Health & Psychosocial Support Hub.
Online Articles
- 「HCI Contributions in Mental Health: A Modular Framework to Guide Practice」, https://kclpure.kcl.ac.uk/portal/en/publications/hci-contributions-in-mental-health-a-modular-framework-to-guide-p
- 「Artificially Intelligent Chatbots in Digital Mental Health」, https://mhpsshub.org/wp-content/uploads/2024/09/Boucher_Artificially-intelligent-chatbots.pdf
- 「Digital Phenotyping for Monitoring Mental Disorders」, https://pmc.ncbi.nlm.nih.gov/articles/PMC10753422/
- 「Harnessing Wearable Technology for Enhanced Depression Treatment」, https://pmc.ncbi.nlm.nih.gov/articles/PMC11374139/
- 「How Virtual Reality Therapy is Shaping Mental Health」, https://www.nature.com/immersive/d41591-024-00032-2/index.html
- 「The Use of Virtual Reality Interventions to Promote Positive Mental Health」, https://mental.jmir.org/2023/1/e44998
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