Oopsie-daisy! - AI Guided Digital Companion for ADHD
By Devanshi Singh Saxena
and Manasa Harish
Attention-Deficit/Hyperactivity Disorder (ADHD) presents difficulties in motivation, task initiation, and emotional regulation, resulting in procrastination and inconsistent productivity. Although Cognitive Behavioral Therapy (CBT) and Dialectical Behavior Therapy (DBT) work, numerous individuals find difficulty with commitment and self-monitering.
This paper explores an AI-based digital companion—a wearable and app-based platform drawing inspiration from Fitbit and Tamagotchi devices—that offers real-time motivation, emotional grounding, and behavioural insights for neurodivergent users. The platform combines AI-powered journaling, sentiment analysis, and behavior prediction to provide personalized interventions. A "BeReal"-inspired daily check-in supports fosters self-awareness and behavioural tracking, with an optional digital community giving peer support and shared understanding.
The AI companion utilizes reinforcement methods to provide micro-tasks, mindfulness exercises, and motivational prompts cognitive load, promoting habit formation and self-regulation. CBT-grounded cognitive restructuring and DBT-informed distress tolerance training deliver real-time coping strategies, and predictive analytics foresees emotional variations to provide timely assistance. The system dynamically modulates its interventions based on user participation, enabling people to remain persistent in therapy regimens.
Therapists gain insight from sentiment analysis and behavioural monitoring, enabling them to detect patterns of emotion and motivation that guide treatment decisions. Ethical factors such as transparency, user permission, and data protection safeguard privacy while supplementing human-led therapy without substituting it. Furthermore, AI-based insights grant autonomy to the user to control ADHD symptoms while retaining accessibility and flexibility.
Evidence cites the efficacy of AI-powered mental health software in building resilience and motivation. Integrating AI-driven journaling, sentiment analysis, and community engagement, this system presents a low-threshold, user-specific, and groundbreaking method for ADHD treatment that connects the divide between conventional therapy and online mental well-being.
Enhancing Traditional Therapeutic Approaches for Tourette syndrome
By Janki U Tandon
Tourette Syndrome (TS) is a neurodevelopmental disorder characterized by involuntary motor and vocal tics that impair social, cognitive, and emotional functioning. Traditional treatments such as Habit Reversal Training (HRT) and Cognitive Behavioral Therapy (CBT) yield benefits but often fall short in helping patients self-regulate tics and manage premonitory urges. Recent advances in digital biofeedback offer promising means to augment these therapies by providing real-time physiological data to patients and clinicians.
A case study by Nagai et al. (2014) examined electrodermal biofeedback as an adjunct to HRT in adolescents with TS. In this study, a 15-year-old male, anonymized as “John,” wore a sensor monitoring skin conductance to detect subtle physiological changes preceding tics. The biofeedback system provided immediate alerts, allowing John to apply competing responses as part of his HRT regimen. Although improvements were similar to those in sham feedback groups, this case underscores the potential of digital biofeedback to enhance self-awareness and reinforce behavioral strategies.
Complementing these findings, Vollmer, Ginsburg, and Leckman (2018) reviewed multiple technology-based interventions for TS and concluded that integrating digital biofeedback with conventional therapies offers adaptive, individualized treatment options. Real-time monitoring of markers such as heart rate variability and galvanic skin response facilitates timely intervention and enables therapists to customize protocols based on observed patterns. This integration can extend therapeutic benefits beyond clinical settings, supporting home-based practice and improving treatment accessibility.
By combining digital biofeedback with traditional modalities, patients are empowered to actively self-regulate while clinicians receive objective, data-driven insights to tailor interventions more precisely. Remote monitoring and home-based practice address common barriers like geographic limitations and scheduling constraints. Ethical implementation is ensured through strict data privacy and the preservation of the therapeutic alliance, with digital tools serving as adjuncts rather than replacements for human interaction.
Collectively, these case studies and clinical reviews suggest that integrating digital biofeedback into traditional therapeutic frameworks may improve outcomes for individuals with TS, warranting further rigorous research to refine these methods.
AI Assisted Rational Emotive Behavior Therapy (REBT) for Autism Spectrum Disorder
By S Samanthira Dhevi
Contexts and challenges
ASD is characterized by social problems, emotional dysregulation, and sensory sensitivity, with minimal therapies available in India. Aarav, a 16-year-old with ASD, has difficulty with social anxiety, emotional expressiveness, and sensory overload, rendering typical therapy useless. It is vital to have a systematic and adaptive solution.
Proposed Intervention
This approach combines REBT with artificial intelligence to make treatment more organized, engaging, and accessible. AI techniques make cognitive reorganization easier, addressing ASD-specific issues.
Technology Integration
• The Emotion-Adaptive AI Companion detects emotions and provides structured REBT instruction based on speech and facial expressions.
• The VR-Assisted Social Simulator provides a controlled environment to practice social interactions with real-time AI feedback.
• Sensory-Responsive Interface reduces sensory overload by adjusting screen brightness, contrast, and noise levels.
• The AI-powered Visual Thought Mapper simplifies communication and emotional expression by converting thoughts into visual representations.
Innovation & Impact
Unlike traditional treatment, this approach adjusts dynamically to real-time emotional and sensory reactions. VR training improves social skills in a low-stress environment, boosting confidence. The AI-powered visual thought mapper makes treatment more accessible, especially in India, where autism support is expanding.
Ethical considerations
Encrypted data and therapist oversight safeguard privacy. Human therapists are complemented by AI rather than replaced. Aarav's autonomy is respected, with organized parental participation to aid advancement.
Conclusion
This REBT paradigm improves engagement and accessibility by combining AI-driven emotional support, VR-based social training, and sensory-adaptive technologies, laying the groundwork for future gamified therapies.
References
Bishop-Fitzpatrick, L., & Jordan, R. P. (2021). AI-driven emotion recognition in autism: Applications and challenges. Parsons, S., & Cobb, S. (2014). State-of-the-art virtual reality technologies for children on the autism spectrum.
Leveraging Advanced Neuro-Adaptive Technology to Optimize Therapy Outcomes
By Shruthi Suresh
Neurodivergent individuals require specialized and adaptive therapeutic interventions tailored to their cognitive, emotional, and sensory processing needs. This case study presents Elias, a 22-year-old diagnosed with autism spectrum disorder (ASD) and Social Anxiety Disorder (SAD), who experiences executive functioning difficulties, sensory processing challenges, and social communication barriers. Conventional therapy has been ineffective due to Elias’s difficulty with real-time emotional articulation and aversion to unstructured therapeutic settings.
We propose a neuro-adaptive intervention combining Quantum Neural Network (QNN)-enhanced sentiment analysis with Multi-Sensory Extended Reality (XR) therapy. Our AI-driven cognitive-affective interface adjusts therapy in real time using biofeedback, reducing cognitive overload and enhancing emotional expression. Trained on neurodivergent linguistic patterns, the QNN refines communication to optimize engagement. XR-based exposure therapy offers a controlled space for Elias to practice social interactions and emotional regulation, adapting sensory input at his own pace.
A key innovation in this model is Predictive Adaptive Response Systems (PARS), which leverage biometric and behavioral analytics to anticipate emotional dysregulation, enabling preemptive, personalized de-escalation. Decentralized encrypted data storage ensures confidentiality, addressing AI-related ethical concerns. Neuro-symbolic AI enhances contextual understanding, adapting therapy in real time to Elias’s cognitive and emotional state. Additionally, a reinforcement learning-driven virtual therapist ensures precise, responsive engagement aligned with Elias’s evolving needs.
This approach is supported by emerging research on AI-driven emotion recognition and XR therapy for neurodivergent individuals. By merging AI-enhanced processing with immersive technology, our framework presents a dynamic, autonomous, and personalized therapeutic paradigm that respects cognitive diversity while maximizing therapeutic efficacy.
References: D’Mello, S., Dietrich, S., & Bosch, N. (2021). AI-Augmented Emotional Intelligence in Therapy: Affective Computing and Its Applications. Journal of Computational Psychology, 36(4), 567-589.Lanier, J., & Biocca, F. (2022). Extended Reality and Neural Adaptability in Therapeutic Contexts: A Review. Neuroscience & Virtual Environments, 45(2), 233-251.
Enhancing Executive Function in ADHD Through AI-Assisted Cognitive Coaching
By Vaanya Issar Suri
Attention-Deficit/Hyperactivity Disorder (ADHD) presents significant challenges in executive function, including working memory deficits, impulsivity, and difficulty with task management. Traditional therapeutic approaches, such as Cognitive Behavioral Therapy (CBT), provide effective coping strategies; however, many individuals struggle with consistent application in daily life. This abstract presents a hypothetical case study of Mia, a 22-year-old university student with ADHD, experiencing chronic procrastination, disorganization, and emotional dysregulation, leading to academic and personal stress.
This paper proposes an innovative, technology-assisted intervention integrating Executive Function Coaching with AI-driven cognitive training and task management. The intervention includes an AI-powered digital assistant that employs machine learning to provide real-time reminders, adaptive goal-setting, and personalized feedback. Additionally, biofeedback technology, such as wearable EEG or heart rate variability monitoring, helps Mia track physiological indicators of stress and attention lapses. This biofeedback data is processed using AI models to suggest real-time self-regulation strategies, such as breathing exercises or structured breaks. Furthermore, the digital assistant analyzes Mia’s task completion patterns and provides nudges to optimize her focus and productivity.
Recent research supports AI-assisted cognitive interventions in improving executive function in ADHD populations. For instance, a systematic review highlighted the impact of AI-powered cognitive training applications in enhancing cognitive processes and executive functions in children with ADHD.
Another study demonstrated that AI-driven digital cognitive programs can reduce impulsiveness and inattentiveness in individuals with ADHD. Moreover, integrating biofeedback techniques, such as neurofeedback, has shown promise in treating ADHD symptoms by improving self-regulation and attention.
Ethical considerations, including data privacy, user control over AI-generated interventions, and therapist oversight, ensure responsible integration of technology into therapy. This AI-assisted intervention offers a promising solution for individuals with ADHD, demonstrating how technology can enhance therapeutic outcomes while maintaining human-centric, ethical mental health support.