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International Journal of Global Mental Health, Innovation, Policy, Action, Culture & Transformation

International Journal of Global Mental Health, Innovation, Policy, Action, Culture & Transformation

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📢 Latest Update: New special issue call for papers on "Global Mental Health, Innovation, Policy, Action, Culture & Transformation" - Submit by March 31, 2026

📢 Latest Update: New special issue call for papers on "Global Mental Health, Innovation, Policy, Action, Culture & Transformation" - Submit by March 31, 2026

Volume 2, Issue 1 - 2026 (Symposium Proceedings: Artificial Intelligence in Psychology & Mental Health. )

Volume 2 Issue 1 Cover

Issue Details:

Volume 2 Issue 1
Published:Feb 5, 2026

Editorial: Symposium Proceedings: Artificial Intelligence in Psychology & Mental Health.

Welcome to the 2026 issue of International Journal of Global Mental Health, Innovation, Policy, Action, Culture & Transformation. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

Dr. Aashna Narula
Editor in Chief
International Journal of Global Mental Health, Innovation, Policy, Action, Culture & Transformation

Articles in This Issue

Showing 31 of 31 articles
Research PaperID: impact-00001216Pages 1

Loneliness and AI dependency

Matrika Duggal

The present study examined the relationship between loneliness and dependency on generative artificial intelligence (AI) among Generation Z individuals. Loneliness was assessed using the UCLA Loneliness Scale, while dependency on AI was measured through the Generative AI Dependency Scale. A sample of 30 participants was selected, and a correlational research design was employed to analyze the association between the variables. The findings revealed a positive relationship between loneliness and dependency on generative AI (p = _), indicating that individuals experiencing higher levels of loneliness tend to show greater reliance on AI tools such as ChatGPT and Gemini. The results suggest that lonely individuals may use generative AI for emotional support, companionship, and the sharing of personal information. The study highlights the growing role of AI in fulfilling social and emotional needs among young adults. Further research with larger and more diverse samples is required to explore the long-term implications of AI dependency and to examine whether generative AI can meaningfully substitute human relationships or merely serve as a temporary coping mechanism.

3,161 views
1,014 downloads
DOI:
10.61113/impact.V2I1.1236

Contributors:

 Matrika Duggal
Research PaperID: impact-00001217Pages 2

The mediating role of metacognitive awareness in the relationship between functional AI use and Critical thinking

Shreya Aggarwal, Anureet Khandpur

The increasing integration of Artificial Intelligence (AI) in the learning environment has raised questions regarding its influence on students’ cognitive and higher-order thinking processes. The present study examined the mediating role of metacognitive awareness in the relationship between critical awareness and functional use of artificial intelligence among young adults. The study used a correlational, cross-sectional design with a sample including 82 young adults in the 18-25 age range. Data was collected using standardized measures to assess metacognitive awareness, critical thinking, and functional AI usage. Pearson correlation analysis and Mediation analysis were conducted to examine the intricate interactions among these factors. The results revealed a significant negative relationship between Functional AI use and Critical Thinking, as well as a significant negative relationship between Functional AI use and Meta-cognitive Awareness. Moreover, the results indicated a significant positive relationship between Meta-cognitive awareness and Critical Thinking. Mediation Analysis confirmed that Meta-cognitive Awareness significantly mediated the relationship between Functional AI usage and Critical Thinking; however, the direct effect was non-significant, indicating full mediation. The findings suggested the need for AI tools that foster metacognitive abilities, which in turn enhance critical thinking.

3,328 views
1,045 downloads
DOI:
10.61113/impact.V2I1.1237

Contributors:

 Shreya Aggarwal
,
 Anureet Khandpur
Research PaperID: impact-00001218Pages 3-19

BRAIN-COMPUTER INTERFACE INTEGRATED NEUROPSYCHOLOGICAL ASSESSMENT FOR QUASI-PSYCHOTIC SYMPTOMS

Palak Shori

In clinical psychology/psychiatry, quasi-psychotic symptoms refer to experiences such as suspiciousness or paranoia, brief hallucination-like experiences, ideas of reference, odd beliefs that are not fixed or delusional, experiences that resolve quickly, and insight usually retained. Studies using structured tools show that subclinical psychotic-like experiences (PLEs) occur in a portion of people in India. The present paper explores the potential of BCI-derived neural markers as objective indicators of quasi-psychotic states. BCI systems enable real-time acquisition and interpretation of neural signals, particularly electroencephalographic (EEG) patterns associated with perception, cognition, and self-referential processing. Alterations in neural oscillations, functional connectivity, and event-related potentials captured through BCI paradigms may reflect disruptions in reality testing, perceptual integration, and cognitive control. In quasi-psychosis, BCI would monitor Neural Oscillatory Dysregulation (including increased theta activity, reduced alpha coherence, and aberrant gamma activity), Event-Related Potential (ERP) alterations (including reduced P300 amplitude and mismatch negativity changes), and Functional Connectivity Shifts (including alterations in prefrontal regions and temporal-parietal regions). BCI systems can track moment-to-moment neural instability, detect onset, intensity, and resolution of quasi-psychotic states, differentiate quasi-psychosis from anxiety or dissociation, full psychosis, and normative stress responses. This is particularly valuable in disorders like borderline personality disorder, trauma-related conditions, and mood disorders, where quasi-psychosis is episodic. The conventional diagnosis using neuroimaging tools such as fMRI and CT scans is time-consuming, and error prone. Hence, Machine Learning models in BCI like Supervised Learning models (Support Vector Machines, Random Forests, Logistic Regression), Deep Learning models (Convolutional Neural Networks, Recurrent Neural Networks, Hybrid CNN-LSTM models), and Unsupervised Learning models (K-Means/Hierarchical clustering, Autoencoders) can be used to generate risk scores, and understand temporal trajectories. On a concluding note, this paper offers an interdisciplinary view of how BCIs can be helpful in assessing quasi-psychotic symptoms.

2,980 views
896 downloads
DOI:
10.61113/impact.V2I1.1238

Contributors:

 Palak Shori
Research PaperID: impact-00001219Pages 4

The Cognitive Cost of Artificial Intelligence: A Neurocognitive Review

Namika Gumber

Artificial Intelligence was originally conceived as a tool for human ease, intended to enhance productivity and streamline complex tasks. However, recent trends suggest a shift from augmentation to a total "cognitive offloading," where humans increasingly outsource critical thinking and memory to AI. This review paper explores the hypothesis that this dependency is contributing to a reversal of the Flynn Effect. After reviewing neuroimaging data from fMRI and EEG studies, the paper identifies significant alterations in neuroplasticity and neural activity. A primary concern is the observed decline in hippocampal volume among individuals who over-rely on AI for routine and complex cognitive tasks. Because the brain follows a "use it or lose it" paradigm, bypassing "desirable difficulties" during information processing leads to a lack of deep memory encoding and weakened retention skills. This phenomenon, often termed "Digital Amnesia," suggests that our neural architecture is physically adapting to a state of passive retrieval rather than active synthesis. The paper concludes that while AI offers immense efficiency, we must develop a balanced Human-AI interaction model. By treating AI as a collaborative partner rather than a cognitive substitute, we can leverage technological speed while preserving the biological integrity and intellectual capacity of the human brain.

3,154 views
1,114 downloads
DOI:
10.61113/impact.V2I1.1239

Contributors:

 Namika Gumber
Research PaperID: impact-00001220Pages 5-12

Artificial Intelligence in Forensic Psychological Risk Assessment: Predicting Criminal Behavior, Bias, and Ethical Boundaries

Brahamjot Singh

The growing use of Artificial Intelligence (AI) in forensic psychology has reshaped the way criminal behavior and psychological risk factors are assessed. Conventional forensic evaluations largely depend on interviews, behavioral observation, and expert judgment, which may be influenced by subjectivity and human bias. AI-driven tools introduce a more systematic and data-oriented approach by examining behavioral patterns, psychological indicators, and past records to support risk assessment. This abstract examines the application of AI in forensic psychological risk assessment, with particular emphasis on predicting violent tendencies, likelihood of re-offending, and mental health vulnerabilities among offenders. A conceptual and review-based methodology has been adopted to analyze existing AI technologies, including machine learning models, predictive analytics, and behavioral assessment systems used within forensic and criminal justice settings. Alongside its advantages, the use of AI raises significant ethical challenges. Issues such as algorithmic bias, lack of transparency, data privacy concerns, and the potential misuse of automated predictions can have serious consequences in legal and mental health contexts. Over-dependence on AI outputs may lead to unfair profiling or compromised judicial decisions if not carefully regulated. The abstract highlights the importance of integrating AI tools with professional judgmentin forensic psychology. Establishing ethical frameworks, ensuring accountability, and maintaining interdisciplinary collaboration are essential for the responsible application of AI in criminal justice. This perspective is particularly relevant for forensic science students and professionals seeking to understand the balanced and ethical use of AI in psychological risk assessment.

3,600 views
1,072 downloads
DOI:
10.61113/impact.V2I1.1240

Contributors:

 Brahamjot Singh
Research PaperID: impact-00001221Pages 6

The Psychological Implications of AI-Driven Short-Form Video Algorithms: Impulsivity and Delay of Gratification in Young adults

Abigail Anna George

With the growing integration of digital media into everyday life, increased engagement with short-form video content has raised concerns regarding attention and self-regulatory functioning amongst young adults. Short-form video platforms present users with rapidly changing, personalized content streams that are often optimized to sustain engagement. Increased consumption of such content has the potential to influence impulsive behaviour and long-term goal-oriented decision-making. Most platforms rely on AI driven algorithmic systems to curate personalized content, filter information, and reinforce users’ beliefs and preferences, creating echo chambers. This results in maximum user engagement and, prolonged viewing durations. This study examined whether time spent consuming such short-form content could act as indicator of various regulatory difficulties like increased impulsivity in behaviour and reduced ability to delay gratification. A sample of 32 young adults, aged 18-26 from Mohali, Punjab, completed a self-report questionnaire assessing impulsivity, the ability to delay gratification and a few questions regarding the number of hours spent watching short-form video content each day. Correlational analysis revealed a significant positive relationship between time spent consuming AI curated short-form video content and impulsivity. This indicated that individuals who spent more time consuming such content reported higher impulsive tendencies. Time spent on short-form video content was also negatively related to delay of gratification, although this association was not statistically significant. These findings suggested that increased exposure to algorithmically recommended short-form video content may be associated with reduced self-control, particularly in the form of heightened impulsivity. These results underscore the need for mindful use of digital media and its potential implications for self-regulatory processes in young adults.

3,563 views
1,063 downloads
DOI:
10.61113/impact.V2I1.1241

Contributors:

 Abigail Anna George
Research PaperID: impact-00001222Pages 7-22

Beyond Translation: Need for Cultural Attunement in AI psychotherapy chatbots

Jiya Sarvpriya

In recent years, the landscape of mental health care has been transformed by the rise of artificial intelligence. One of the most talked-about innovations is AI-based psychotherapy chatbots, which gained popularity due to a global shortage of psychotherapists (WHO, 2020), their easy and low-cost availability, and growing human-robot interactions (HRI) in the digital world. Tools such as Woebot, Wysa, Replika offer users 24/7 support, rooted in Cognitive Behavioural Therapy (CBT), mindfulness and other psychoeducation tools. While research has explored their effectiveness, there are certain cultural implications which remain underexamined. For instance, when an Indian user seeks advice regarding facing family pressure, the standardized AI response often prioritizes individualism and suggests setting boundaries. This is incongruent with the collectivistic vision of Indian society. The paper discusses how most of the algorithmic training of AI chatbots is done in a Western context. It talks about “Monorhythmic Algorithm”: a system which uses a standard logic to respond even for culturally diverse users. In contrast, norms, distress and help-seeking behaviours are culturally polyrhythmic and even the most accurate AI system may misinterpret the underlying meanings and offer advice that is irrelevant or rather harmful in non-Western contexts. While discussing the research gap, the paper further argues that although there have been advancements such as inclusion of customized local languages in platforms like Wysa, there is a need for the existing AI algorithms to incorporate a layer of cultural transparency and culture-sensitive responses that prioritize the users’ lived realities. It concludes by proposing some strategies such as cultural context embedding, cultural transparency prompts, local humans-in-the-loop, collaboration with AI developers and cultural audits that can be included to mitigate cultural barriers and promote culturally attuned mental health care.

3,738 views
1,075 downloads
DOI:
10.61113/impact.V2I1.1242

Contributors:

 Jiya Sarvpriya
Research PaperID: impact-00001223Pages 8

RELATIONSHIP OF GENERATIVE AI DEPENDENCY WITH WORK ALIENATION AND NEED FOR COGNITION AMONG WORKING PROFESSIONALS

Harmanjot Kaur

The growing integration of generative Artificial Intelligence (AI) in workplaces has transformed the way professionals think, learn and perform tasks. While AI enhances efficiency and accessibility, increasing dependence on it may influence employee’s cognitive engagement and their psychological connection with work. The present study aims to examine the relationship of Generative AI Dependency with Work Alienation and Need for Cognition among working professionals. The study is grounded in the assumption that greater reliance on AI may be associated with increased alienation from work and reduced inclination towards cognitive effort. It is hypothesized that Hypothesis 1: there will be a significant positive relationship between Generative AI Dependency and Work Alienation and Hypothesis 2: there will be a significant negative relationship between Generative AI Dependency and Need for Cognition. A quantitative correlational design will be employed and data will be collected using standardized measures: the Generative AI Dependency Scale (Goh, Hartanto & Majeed, 2025), the Work Alienation Scale (Nair & Vohra, 2010) and the Need for Cognition Scale (Cacioppo, Petty & Kao, 1984), each scale consisting of 11, 8 and 18 items respectively. The questionnaires will be administered through Google Forms to a sample of working professionals. Pearson correlation will be used to analyze the relationship among variables. The study seeks to contribute to emerging literature on the psychological implications of AI usage by exploring whether reliance on generative AI is associated with greater feelings of detachment from work and reduced inclination towards cognitive effort. Findings are expected to inform balanced human-AI interaction strategies that preserve critical thinking and employee’s sense of meaning within organizational settings.

3,826 views
1,209 downloads
DOI:
10.61113/impact.V2I1.1243

Contributors:

 Harmanjot Kaur
Research PaperID: impact-00001224Pages 9

AI-Narrated Awakening: Boosting Storytelling and Mental Health Against Digital Overwhelm

Aditi Trikha

Artificial Intelligence revolutionizes narrative therapy by functioning as a scalable "story coach" that generates personalized growth narratives from user inputs. AI platforms analyze social media stress and craft reframed empowerment tales, transforming Instagram-induced anxiety into strength-building arcs. While AI expands mental health accessibility through digital interventions, proliferating technologies simultaneously intensify information overload and psychological distress. This study examined how AI-narrated storytelling interventions influence mental health outcomes among individuals experiencing digital fatigue. AI-Narrated Awakening is conceptualized as a transformative process wherein AI-assisted narratives facilitate cognitive and emotional reorientation, enabling individuals to develop self-awareness, psychological resilience, and renewed perspective through reframed personal stories (Anderson & Chen, 2023). Storytelling Engagement encompasses cognitive absorption, emotional involvement, attentional focus, and narrative presence when interacting with story content (Green & Brock, 2000; Busselle & Bilandzic, 2009). Personal Growth refers to intentional, self-directed development of one's potential and psychological maturity through transformative experiences (Robitschek et al., 2012). Mental Health encompasses emotional, psychological, and social well-being (World Health Organization, 2022), while Digital Overwhelm represents a state of psychological and cognitive exhaustion resulting from excessive exposure to digital information and communication technologies (Reinecke et al., 2017; Dhir et al., 2018). The aim of the present study is to examine the relationship and gender differences in AI-narrated storytelling's effectiveness for enhancing personal growth and wellbeing among high social media young adults, while accounting for digital overwhelm effects. The sample consisted of 100 participants aged 18-35 years drawn from university students and young professionals with high digital media usage. The study employed the Narrative Engagement Scale (NES), Social Media Fatigue Scale (SMFS), Personal Growth Initiative Scale-II Readiness subscale (PGIS-II), and WHO-5 Well-Being Index. The results were found significant and were discussed later.

4,103 views
1,171 downloads
DOI:
10.61113/impact.V2I1.1244

Contributors:

 Aditi Trikha
Research PaperID: impact-00001225Pages 10

Development and Validation of a Multimodal AI Framework for Assessing Craving Intensity and Vedic Personality Traits in Substance Use Disorders

Anushree Rath

The clinical evaluation of craving in the context of Substance Use Disorders, is still heavily dependent on subjective self-reports, which are frequently tainted by patient anosognosia and social desirability bias, despite advancements in addiction science. Moreover, the qualitative internal states described by Indian Knowledge Systems (IKS) and the subtle neurocognitive antecedents of relapse outlined by the Binding and Retrieval in Action Control (BRAC) model are not captured by existing diagnostics. By creating a ground-breaking Multimodal AI Fusion Framework, this study fills this diagnostic gap. The goal of the study is to translate the abstract philosophical concepts of Triguna, more especially, the transition from Tamasic to Sattvik balance, into objective, measurable bio-behavioral indicators. The study uses a unique,non-invasive web-based battery that synchronizes three different data streams to produce a composite Relapse Risk Index using a cross-sectional tool-development approach (N=50). First, computer vision oculometrics calculates gaze entropy using common webcams to understand the hypervigilant scanning behaviour typical in high craving states. Second, in order to identify the microscopic motor rigidities linked to maladaptive event file binding, kinematic analysis records mouse cursor movements during cognitive interference tasks, similar to an Alcohol stroop test, by examining trajectory curvature and velocity peaks. Third, speech recordings are analyzed by Acoustic and Semantic NLP to determine the prosodic signs (jitter/shimmer) of Sattvik clarity against Tamasic depression. This study goes beyond conventional psychometrics by creating a machine learning classifier to merge various physiological information. The Vedic Lifestyle Scale and the Obsessive Compulsive Drinking Scale (OCDS) are used to validate these algorithmic features. By developing a scalable, culturally based methodology, the study makes a significant addition to precision psychiatry. It makes the case that effective rehabilitation involves more than just quitting drugs; rather, it involves a quantifiable change in neuro-cognitive economy, which can now be identified, measured, and tracked thanks to the convergence of artificial intelligence and traditional psychology theory.

3,937 views
1,297 downloads
DOI:
10.61113/impact.V2I1.1245

Contributors:

 Anushree Rath
Research PaperID: impact-00001226Pages 11-27

Motivated Non-Use in AI-Based Mental Health Interventions: A Structural Readiness Account of Engagement Failure

Nikesh Lagun

Digital mental health interventions (DMHIs), including AI-enabled applications, chatbots, and digital cognitive behavioural therapy platforms, have expanded rapidly as scalable approaches to improving access to care. Despite demonstrated efficacy under controlled conditions, real-world impact remains constrained by persistent engagement failure and early discontinuation. Many users disengage shortly after adoption, even when they report valuing the intervention and intending to use it. Existing explanations of engagement failure primarily emphasise motivation, usability, or perceived usefulness; however, these frameworks provide limited insight into why motivated users often fail to initiate or re-initiate engagement. Drawing on a narrative review of the digital mental health literature, this paper argues that engagement breakdown frequently occurs at the level of action initiation rather than intention formation. A structural readiness account is proposed, grounded in activation dynamics and threshold-dependent ignition processes formalised in Lagun’s Law within Cognitive Drive Architecture. From this perspective, non-use reflects a readiness mismatch between preserved intention and insufficient momentary activation, rather than a deficit of motivation or interest. The paper outlines implications for readiness-aware and ethically responsible AI design, highlights relevance for clinical interpretation and policy evaluation, and proposes empirically testable predictions for future research. By reframing engagement failure through activation-dependent threshold dynamics, this work advances a mechanistic foundation for understanding and improving engagement in AI-based mental health interventions.

4,193 views
1,217 downloads
DOI:
10.61113/impact.V2I1.1246

Contributors:

 Nikesh Lagun
Research PaperID: impact-00001227Pages 28-39

Does Explicitly Stating ‘AI Lacks Consciousness’ Increase Anthropomorphic Perceptions

Partishtha Sharma, Kaveri Bajaj

Machines are thinking, but do they actually know they are thinking? That’s what Alan Turing says. Today's machines working in the human world make us think about different aspects of human existence. With the emergence of Generative AI & its radical transformation, human interactions with Artificial Intelligence have consequently increased. These increased interactions lead the users to tend to “humanize” or attribute “human-like” traits/qualities to AI tools, termed as Anthropomorphism. The present study aimed to explore whether explicitly informing users that ‘AI lacks consciousness’ can alter the already existing view of Anthropomorphism among them. The study is grounded in the concept of “Psychological Reactance”, suggesting that when told ‘AI lacks consciousness’, it paradoxically heightens the perceptions of Anthropomorphism, Likeability & Perceived Intelligence in AI among them. For this, data was collected from people ranging from ages 18 to 30 years, regularly using AI and randomly assigning them to two groups: Group A, told explicitly that ‘AI lacks consciousness’ and no such warnings to Group B. The Godspeed Questionnaire Series (Bartneck et al.) sub-scales, specifically Likeability, Perceived Intelligence & Anthropomorphism, were used to measure the differences of perception between the two groups. The findings of the study are expected to contribute to a better understanding of how the provision of explicit information or ‘warnings’ affects perceptions of people towards AI.

4,367 views
1,409 downloads
DOI:
10.61113/impact.V2I1.1247

Contributors:

 Partishtha Sharma
,
 Kaveri Bajaj
Research PaperID: impact-00001228Pages 13

Attachment Styles Towards Artificial Intelligence: Exploring Emotional Bond Formation Among College Students

Jashanpreet Kaur

Artificial intelligence (AI) has increasingly transitioned from a functional technological tool to a psychologically meaningful presence in the daily lives of college students. Beyond academic assistance, AI systems are now frequently used for emotional expression, companionship, and non-judgmental support, raising important questions regarding emotional bonding and attachment-like relationships with non-human agents. Drawing on attachment theory, the present study aims to examine how attachment styles—specifically attachment anxiety and attachment avoidance—are associated with emotional bonding, trust, and reliance on AI among college students. The study adopts a quantitative research design with a sample of 60 undergraduate students aged 18–23 years from a university in Mohali, India. Data will be collected using an adapted version of the Experiences in Close Relationships (ECR) Scale to assess attachment anxiety and avoidance toward AI, along with selected subscales of the Godspeed Questionnaire Series measuring anthropomorphism, likeability, and trust. Responses will be obtained through online questionnaires, and descriptive and correlational analyses will be conducted to explore relationships between attachment dimensions and emotional engagement with AI. It is anticipated that students with higher attachment anxiety will report stronger emotional bonds, greater trust, and increased reliance on AI for emotional support, whereas attachment avoidance may be associated with more instrumental and emotionally distant patterns of use. The expected findings aim to position AI not merely as a technological tool but as an emerging relational entity in students’ psychological lives. This study contributes to the growing literature on human–AI interaction by applying attachment theory to understand evolving emotional dynamics, while also highlighting implications for mental health practice, ethical AI design, and the prevention of over-reliance on artificial agents.

4,577 views
1,295 downloads
DOI:
10.61113/impact.V2I1.1248

Contributors:

 Jashanpreet Kaur
Research PaperID: impact-00001229Pages 14-25

AI Supported Mental Health Literacy and Action Competencies Among Special and Mainstream Teachers in Inclusive Primary Schools

Divya Rose Peter, Ebrahim Nangarath Kottakal Cheriya

AI-supported mental health literacy and action competencies among special education and mainstream teachers in inclusive primary schools are critical for promoting early identification and support of students’ psychological needs. This abstract proposes a study that examines teachers’ knowledge, attitudes, and skills in using artificial intelligence (AI) tools to recognize, respond to, and refer mental health concerns in inclusive classroom settings. The study will adopt a mixed-methods design, combining a survey of special and mainstream primary school teachers with in-depth interviews to explore how AI-enabled platforms, such as early warning systems and digital screening tools, are integrated into daily pedagogical and pastoral practices. Quantitative data will assess levels of mental health literacy, perceived AI self-efficacy, and action competencies, while qualitative data will capture teachers’ lived experiences, contextual challenges, and culturally grounded concerns. Findings are expected to highlight gaps in AI-related competencies, variations between special education and mainstream teachers, and the influence of school policies and support structures on responsible AI use for student well-being. The study aims to inform evidence-based professional development, ethical and context-sensitive guidelines, and mental health policies that strengthen the role of teachers as front-line partners in AI-augmented school mental health systems within inclusive primary education.

4,349 views
1,422 downloads
DOI:
10.61113/impact.V2I1.1249

Contributors:

 Divya Rose Peter
,
 Ebrahim Nangarath Kottakal Cheriya
Research PaperID: impact-00001230Pages 15

Efficient Depression Detection based on Encoder-only Transformer Architecture: A Review

Shreeya Mishra, Baljeet Kaur

Depression is a major depressive disorder that is defined as a common, yet serious, mental disorder causing persistent sadness and loss of interest for a longer period of time. This eventually affects how an individual feels, behaves, understands, and handles day-to-day life activities and results in emotional and physical drain of energy, and may lead to suicidal thoughts. Early detection of depressive symptoms is essential for timely intervention. However, traditional diagnostic methods are often subjective and resource-intensive. This study aims to analyse the different successful techniques used in depression detection using Artificial Intelligence. Clinical interview-based datasets, such as Daic-woz, E-Daic, and from various social media platforms such as Twitter, Reddit, etc., are considered for this review study. The review comprises a comprehensive study of traditional Machine Learning techniques, such as Support Vector Machines, Random Forests, Logistic Regression, etc., that rely on handcrafted linguistic features. These techniques show limited capability in capturing contextual and emotional nuances. However, feature representations portrayed by Deep Learning techniques such as Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Units have improved the performance metrics. But they too struggle in capturing contextual semantics present in natural language. These limitations have motivated this study to inspect the potential of Transformer-based architectures. The gradual shift to Transformers is due to their self-attention mechanism and ability to capture global contextual relationships within the text, which further forms the backbone of modern Natural Language Processing. The study presents an in-depth analysis of the encoder-only architectures, as they utilize bidirectional self-attention that helps in identifying subtle linguistic markers, understanding contextual embeddings, and capturing deep semantic meaning. Through this review, a comprehensive chronological advent of effective Transformer techniques and their performance has been assimilated.

4,774 views
1,416 downloads
DOI:
10.61113/impact.V2I1.1250

Contributors:

 Shreeya Mishra
,
 Baljeet Kaur
Research PaperID: impact-00001231Pages 16-23

Students’ Views Regarding the Use of AI Tools in Education

Samridhi Thukral

Understanding students' psychological opinions of AI-based tools has become essential in educational and mental health contexts as artificial intelligence continues to have an increasing impact on learning environments. This study looked at undergraduate students' opinions about the perceived value of AI tools in the classroom, the learning support they offer, and how these aspects affect students' attitudes toward their use. Using a quantitative research design, the study was conducted with a sample of 45 undergraduate students from St. Andrew's College in Bandra, Mumbai. A self-report questionnaire with ten items measuring perceived usefulness, learning support, and attitude toward AI tools was used to gather data. All three variables showed significant positive correlations, according to Pearson's correlation analysis. Additionally, a significant amount of the variance in students' attitudes toward AI tools was explained by the combination of perceived usefulness and learning support, according to multiple regression analysis. When taken into account in conjunction with usefulness, learning support showed a positive but non-significant contribution, but perceived usefulness emerged as a significant predictor of attitude. The results imply that perceived academic value, rather than just support, is the main factor influencing students' acceptance of AI tools. These findings demonstrate how attitudes, motivation, and engagement with AI-driven learning tools are shaped by perceived utility from a psychological standpoint. The study emphasizes how crucial it is to carefully and ethically incorporate AI technologies into learning environments in order to improve student psychological engagement and well-being

4,908 views
1,569 downloads
DOI:
10.61113/impact.V2I1.1251

Contributors:

 Samridhi Thukral
Research PaperID: impact-00001232Pages 17

Effectiveness of AI Chatbots in Mental Health Support

Mannat, Snehil, Pragya

With the increasing use of artificial intelligence in healthcare, AI-based chatbots have emerged as a new tool for mental health support. These chatbots are designed to provide users with easy access to basic psychological assistance through text-based conversations. This study focuses on evaluating the effectiveness of AI chatbots in supporting mental health, particularly in reducing symptoms of stress, anxiety, and depression, and in promoting emotional awareness. It also examines important factors that affect their effectiveness, such as personalization, empathy in responses, constant availability, and ethical issues including data privacy and reliability. The study adopts a mixed-method approach, using user surveys, findings from previous research, and comparisons with traditional mental health support systems. The results indicate that while AI chatbots cannot replace professional mental health care, they can serve as useful supplementary tools, especially for early-stage support, self-help, and mental health awareness. The study highlights the need for responsible AI development and regular evaluation to ensure these technologies are used safely and effectively.

5,001 views
1,443 downloads
DOI:
10.61113/impact.V2I1.1252

Contributors:

 Mannat
,
 Snehil
,
 Pragya
Research PaperID: impact-00001233Pages 18

Family Communication in the Era of Algorithm-Driven Social Media: A Qualitative Study of Women Across Age Groups

Simran Arora

Background: Social media is a medium of communication that takes place on the internet. Users can hold discussions, exchange information, and produce online content on social networking sites. Blogs, microblogs, wikis, social networking sites, photo-sharing sites and other types of social media that exist on the internet. Objectives: This study investigates the impact of social media on family communication patterns from the perspectives of women belonging to different age groups. It explores both positive and negative aspects of social media usage within families and identifies strategies for improving family communication. Methodology: Data was collected from two groups: young women aged 18-21 and older women aged 45-55 through Interviews. The findings reveal a significant shift in communication dynamics, with social media facilitating long-distance communication but also contributing to reduced interaction within families. Results: Themes like increased isolation, decreased family communication, and the portrayal of false images on social media emerge consistently across both age groups. Strategies such as setting boundaries, limiting phone usage, and spending quality time together emerged as potential solutions. Conclusion: The themes that emerged from the interview conducted on both the women, demonstrated a similar pattern of impact that has been caused due to the usage of social media in everyday life, where on one hand it has led to positive changes such as increased learning and exploration, finding out one's hobbies and working towards its growth, and ease in communication but has also led to decreased family communication, increased isolation, staying occupied on these platforms etc. which signifies the distance that has been created due to the advent of social media into an individual’s life and how it has played a major role in affecting the overall family communication patterns.

4,946 views
1,539 downloads
DOI:
10.61113/impact.V2I1.1253

Contributors:

 Simran Arora
Research PaperID: impact-00001234Pages 19

Public Health Perspectives on AI-Driven Mental Health Support for Children with Special Needs

Dr. Raskirat Kaur

Artificial Intelligence (AI) is transforming mental health service delivery within public health systems, offering innovative tools for assessment, intervention, and policy-level decision-making. Children with special needs—including those with developmental, learning, and neurodiverse conditions—often face challenges in accessing timely mental health support, leading to delayed identification of emotional and behavioral difficulties and widening disparities in care. AI-driven mental health support provides opportunities to bridge these gaps by enabling early detection, personalized interventions, and continuous monitoring of psychological well-being. Aligned with the World Health Organization (WHO) framework, which emphasizes mental health promotion, prevention, early intervention, and community-based care, AI applications such as predictive analytics, digital screening tools, and adaptive therapeutic platforms can enhance the accuracy and efficiency of mental health assessments. These technologies also allow mental health professionals and educators to monitor progress, adjust interventions in real time, and provide scalable support in school and community settings. Similarly, the National Education Policy (NEP) 2020 highlights inclusive education, early identification of learning difficulties, and the integration of technology to provide personalized learning experiences. AI can support these goals by facilitating individualized educational and psychological plans, improving access to assistive technologies, and enhancing engagement for learners with special needs. While the potential benefits of AI are significant, ethical, legal, and policy challenges must be addressed. Issues such as data privacy, algorithmic bias, informed consent, equitable access, and over-reliance on technology require careful consideration. Human oversight, interdisciplinary collaboration, and evidence-based regulation are critical to ensuring that AI tools complement, rather than replace, traditional mental health services. This research work aims to explore AI-driven mental health support from a public health perspective, emphasizing the integration of technological innovation with ethical practice, inclusive education, and policy frameworks. By examining the intersection of AI, mental health, and special education, the discussion seeks to advance strategies for responsible, equitable, and effective mental health support for children with special needs, ensuring their holistic development and psychological well-being at a population level.

4,995 views
1,490 downloads
DOI:
10.61113/impact.V2I1.1254

Contributors:

 Dr. Raskirat Kaur
Research PaperID: impact-00001235Pages 20-28

THE PARADOX OF DIGITAL CONNECTIVITY: EXAMINING THE RELATIONSHIP BETWEEN ATTENTION, FOMO AND SOCIAL MEDIA INTEGRATION IN THE ERA OF AI ALGORITHMS AMONG YOUNG ADULTS

Srishti Verma

With the vigorously flourishing existence of technology and the growing integration of Artificial Intelligence (AI) in social media platforms through algorithmic content filtering and tailored digital content, young adults are seen to be digitally connected with one another, yet experience heightened anxiety and distractibility. As digital platforms, especially social media, are designed to attract users’ attention, usage patterns indicate that habitual and dysregulated use is associated with numerous psychological, social, and physical developmental problems. The Fear of Missing out (FOMO) substantially increases problematic and compulsive usage of social media, while conversely, reducing attention, resultant of which is poor present moment awareness. Prior research indicates that FOMO and AI were associated with heightened anxiety and depressive symptoms, which in turn reduced overall well-being (Chi-Lin Yu 2025). Attention is conceptualized as the capacity to maintain mindful, present moment awareness and intentional focus amid competing internal and external stimuli, according to Brown and Ryan (2003). FOMO is understood as a persistent worry that others are participating in rewarding experiences from which one is absent, driving a compulsive need to remain connected in order to avoid feelings of social exclusion and disconnection from others. Social media integration refers to the extent to which an individual’s social interactions, emotional experiences, and daily activities are intertwined with their use of social media. The current study attempts to investigate the relationship between Attention, FOMO, and Social Media Integration among young adults in the context of AI-driven digital environments. The total sample is 60, equally divided among females and males. To assess these constructs, the study employs three standardized instruments: Mindful Attention Awareness Scale (MAAS), Fear of Missing out Scale (FoMOS), and Social Media Use Integration Scale (SMUIS). Mean and Standard Deviation (SD) were calculated in addition to correlation and t-ratio for finding out the results.

5,472 views
1,689 downloads
DOI:
10.61113/impact.V2I1.1255

Contributors:

 Srishti Verma
Research PaperID: impact-00001236Pages 21

Use of AI Chatbot in Mental Health Support

Chahat Sharma, Gursheesh, Prof.(Dr.) Renu Vij

Mental health problems such as stress, anxiety, and depression are increasing worldwide, while access to professional mental health services remains limited. In recent years, Artificial Intelligence (AI) chatbots have emerged as supportive tools for mental health care by offering conversation-based assistance. These chatbots use technologies like Natural Language Processing to interact with users, provide emotional support, suggest coping strategies, and promote mental well-being. AI chatbots are easily accessible, cost-effective, and available 24/7, making them especially useful for individuals who hesitate to seek traditional therapy due to stigma or financial constraints. However, despite their advantages, AI chatbots have certain limitations, including lack of human empathy, inability to handle severe mental health conditions, and concerns related to data privacy and ethics. This paper examines the role of AI chatbots in mental health support, highlighting their benefits, limitations, and ethical considerations. The study concludes that AI chatbots can serve as effective supplementary tools, but they should not replace professional mental health practitioners.

5,268 views
1,686 downloads
DOI:
10.61113/impact.V2I1.1256

Contributors:

 Chahat Sharma
,
 Gursheesh
,
 Prof.(Dr.) Renu Vij
Research PaperID: impact-00001237Pages 22-27

Balancing Technology and Empathy: Ethical Considerations in AI-Assisted Counseling

Charul Mehta

With the growing integration of artificial intelligence (AI) into our lives we are becoming totally dependent on it even for our emotional needs as well. Today, the traditional form of mental health services has been transformed through the use of chat bots, virtual mental health platforms and AI assisted counseling practices. Even though these technological advancements can assist in more efficient, accessible and continuous mental health care services, they also raise serious ethical and psychological concerns. These tools can never replace human connection, empathy and emotional security in a therapeutic setting. This review aims to examine the ethical considerations in balancing Technology and Empathy in AI-assisted counseling. After going through existing literature from counseling psychology, mental health ethics, and digital health research, the review synthesizes main ethical and psychological themes, including confidentiality and data privacy, informed consent, professional accountability, therapeutic alliance, emotional attunement, and cultural sensitivity. Special attention is given to how AI-mediated interactions may influence empathy, therapist self-awareness, and the quality of the counseling relationship. The review also highlights concerns related to algorithmic bias, over-reliance on automated systems, and the potential risk of depersonalization within therapeutic processes. The review emphasizes the importance of adopting a human-centered and ethically informed approach to the integration of AI in counseling practice, positioning AI technologies as supportive tools rather than substitutes for human therapists. It underscores the need for ethical guidelines, practitioner training, and reflective practice to ensure responsible and psychologically sound use of AI-assisted interventions. By consolidating current perspectives, this review contributes to ongoing discourse on ethical AI integration in counseling psychology and offers practical insights for mental health professionals, educators, and policymakers navigating the evolving intersection of technology and therapeutic care.

5,410 views
1,746 downloads
DOI:
10.61113/impact.V2I1.1257

Contributors:

 Charul Mehta
Research PaperID: impact-00001238Pages 23

A Comparative Study of Generational Workforce Adaptation to AI Among Generation X and Millennials

Calista Bastian, Dr. Sujata Bhau

Background: Rapid technological advancements have modified the nature of employment, presenting both opportunities as well as difficulties for all generations. The often use of automation and digital tools by organizations has further raised concerns with regards to various aspects of work life among employees, especially Generation X and Millennials. Therefore, it is essential to comprehend and compare how these elements affect both the generations differently whilst managing a multigenerational workforce. Objectives: The study compares Gen X and Millennials on five core dimensions: de-skilling, reskilling, redundancy, tech-driven changes, and job loss anxiety, to investigate notable generational disparities. Methodology: Data collection took place via an online google form to gather information from working individuals of Indian nationalities, belonging to the Generation X (n = 50) and Millennial (n = 50) generations. Independent sample t-test was used for data analysis. Results: The findings depicted a significant difference in dimensions of de-skilling, redundancy and job loss anxiety among the two generations, with Generation X having higher scores than Millennials. On the other hand, there was no statistically significant difference between Generation X and Millennials with regards to tech-driven changes and reskilling. Conclusion: The results of this study will be helpful in developing organizational strategies and interventions meant to assist various generations in adjusting to changes in their workplaces brought about by technological advancements.

5,737 views
1,698 downloads
DOI:
10.61113/impact.V2I1.1258

Contributors:

 Calista Bastian
,
 Dr. Sujata Bhau
Research PaperID: impact-00001239Pages 24

A Framework-Guided Analysis of Trauma-Informed Communication in AI Mental Health Systems

Harini Nanthitha P.S, Nandha Kumar, Navya Ganesh

The growing adoption of artificial intelligence in mental health support systems has raised ethical concerns regarding how these technologies respond to trauma related disclosures and therefore this study examined the extent to which AI-based mental health systems demonstrate alignment with core principles of trauma-informed communication. To aim this,a qualitative exploratory design was employed using six to eight standardized, hypothetical trauma-related scenarios presented consistently across two to three publicly accessible AI mental health systems. AI-generated responses were analysed using reflexive thematic analysis guided by the Substance Abuse and Mental Health Services Administration (SAMHSA) Trauma-Informed Care framework. The analysis revealed that AI systems frequently employed empathetic and validating language, particularly in relation to emotional acknowledgment and perceived safety. However, key trauma-informed principles such as empowerment, collaboration, and user choice were inconsistently demonstrated. In some instances, efforts at emotional containment risked neutralisation or distancing of traumatic experiences.Taken together these findings highlight critical ethical and communicative gaps in AI-mediated trauma responses and underscore the need for trauma-informed principles to be more systematically and intentionally integrated into the design and deployment AI mental health support systems.

5,893 views
1,718 downloads
DOI:
10.61113/impact.V2I1.1259

Contributors:

 Harini Nanthitha P.S
,
 Nandha Kumar
,
 Navya Ganesh
Research PaperID: impact-00001240Pages 25

Artificial Intelligence in Psychological Assessment: Advancing Precision and Individualisation in Autism

Niharika Dadoo, Praisy K. Prabha, Shiv Charan, Rajni Sharma, Ajay Prakash, Bikash Medhi

Psychological assessment in mental health has evolved significantly, with standardised tools and clinical observations providing structured and reliable ways to understand cognitive, emotional, and behavioural functioning. Building on these foundations, recent advances in artificial intelligence (AI) offer additional opportunities to complement existing assessment approaches by enabling more objective, performance-based, and child friendly evaluations. Within this broader landscape, autism spectrum condition represents a particularly meaningful area for the application of AI-enabled assessment. In children, assessment often relies on parent-reported measures, as self-report questionnaires may be developmentally challenging due to limited vocabulary, attention, or engagement. AI-based approaches enable performance-driven and observation based assessment by analysing real-time behavioural data including facial expressions, eye gaze, motor patterns, repetitive movements, and speech characteristics. Analysis of home videos and naturalistic interactions allows for the capture of everyday behaviours that may not be evident in structured clinical environments. Non-invasive wearable devices can further support continuous monitoring by tracking physiological indicators such as heart rate variability and skin conductance, providing insights into sensory processing, stress regulation, and emotional arousal. Speech-based AI systems can detect changes in language, memory, and communication over time, while interactive AI-enabled toys and digital platforms can support speech development by breaking words into simple, monosyllabic components suitable for young children. Ongoing interdisciplinary research continues to refine AI-based approaches, with growing evidence supporting their feasibility, clinical relevance, and ability to support individualised assessment based on each child’s unique symptom profile. Ethical and clinical considerations remain central, including bias in training datasets, explainability of AI systems, risks of over-medicalization, and cultural validity particularly in low and middle income contexts. Effective implementation will also require adequate infrastructural resources, clinician training, and adaptability to diverse clinical and community settings. AI should be viewed as a complementary tool that augments rather than replaces clinical judgment, supporting more precise, scalable, and child-centered assessments.

6,150 views
1,755 downloads
DOI:
10.61113/impact.V2I1.1260

Contributors:

 Niharika Dadoo
,
 Praisy K. Prabha
,
 Shiv Charan
,
 Rajni Sharma
,
 Ajay Prakash
,
 Bikash Medhi
Research PaperID: impact-00001241Pages 26

A Quantum-Assisted Framework for Emotional Understanding in Text

Dharun Ramesh

Quantum computing is typically associated with domains such as cybersecurity, drug discovery, finance, and large-scale algorithmic optimization. While these applications drive technological progress, they remain largely disconnected from the emotional and psychological realities that shape everyday human experience. To enhance personal relevance, quantum systems can instead be applied to support daily emotional well-being, guiding individuals through complex affective states and fostering more harmonious, enriched lives. This study proposes an AI framework that leverages quantum optimization to interpret and classify emotional expressions from text, expanding computational capacity to understand aspects of human consciousness and contextual affect in synchrony with lived experience. By embedding quantum-enhanced affective computation into daily interactions, the model moves beyond predictive analytics to form a dynamic interface with emotional cognition, with potential applications in counselling, self-awareness, and therapeutic support across clinical and organizational settings. Using the HappyDB dataset, containing over 100,000 self-reported happy moments annotated with emotional categories, we demonstrate that conventional AI approaches struggle to capture subtle context and interdependent emotional cues. Our method addresses this gap by integrating classical neural architectures with a quantum variational layer, enabling synchronous evaluation of complex semantic and affective relationships in text. The hybrid model transforms text into semantic embeddings, processes them through sequential LSTM layers, and optimizes latent emotional representations via quantum-inspired techniques. This allows the system to emulate human-like context awareness and achieve enhanced emotion classification beyond the capabilities of classical methods. By applying quantum computing to daily-life emotional cognition at scale, this work reframes quantum algorithms as tools for enriching human experience. Beyond mental health assessment and digital therapeutics, it establishes a new frontier for human-centered quantum intelligence, demonstrating that complex psychological and semantic interactions can be optimized to advance AI-assisted well-being.

5,862 views
1,793 downloads
DOI:
10.61113/impact.V2I1.1261

Contributors:

 Dharun Ramesh
Research PaperID: impact-00001242Pages 27

Mobile Applications for Cognitive and Emotional Health Monitoring in Geriatric Populations: A Review

Prerna Singh, Dr. Tamanna Saxena

The growing incidence of cognitive and emotional disorders among the geriatric population necessitates effective monitoring and intervention strategies. Mobile health applications have emerged as promising tools due to their accessibility, affordability, and potential to support cognitive decline, depression, and anxiety in older adults. However, current applications predominantly address cognitive and emotional domains separately, lacking integrated, holistic approaches tailored to the unique needs of elderly users. Significant research gaps persist in personalization, cultural adaptation, and user engagement, with most applications developed without active participation from older adults. This results in usability challenges and limited long-term adherence. Moreover, existing studies are largely confined to high-income countries, limiting the generalizability of findings across diverse cultural and socioeconomic contexts. This review employs a systematic secondary research methodology, following the PRISMA model, analyzing over 100 peer-reviewed articles and authoritative sources retrieved from Scopus, Google Scholar, and specialized healthcare databases. The study identifies critical gaps and opportunities for advancing mobile health technologies for geriatric cognitive and emotional well-being. Results indicate substantial potential for mobile applications to improve geriatric health outcomes, contingent on addressing current limitations in design inclusivity and contextual adaptation. Future research should prioritize participatory, user- centered design approaches that integrate cognitive and emotional health metrics within culturally adaptable platforms. Large-scale, longitudinal validation studies are essential to confirm effectiveness and sustainability. Through these enhancements, mobile health applications can evolve into comprehensive, inclusive tools that foster cognitive resilience and emotional well-being in aging populations globally.

6,306 views
1,900 downloads
DOI:
10.61113/impact.V2I1.1262

Contributors:

 Prerna Singh
,
 Dr. Tamanna Saxena
Research PaperID: impact-00001243Pages 28

The Impact of Sleep Deprivation on Cognitive Performance and Emotional Regulation: A Cross-Sectional Analysis of Middle Eastern Adults

MD. Faisal Ahmed

Background: Sleep deprivation is a widespread issue with known negative effects on cognitive performance and emotional regulation. However, the relationship between these factors, particularly in Middle Eastern populations, remains understudied. Objectives: This study examines the effects of sleep deprivation on cognitive tasks and emotional regulation in a sample of 60 Middle Eastern adults. Methods: Data were collected from 60 participants, assessing variables such as sleep hours, cognitive performance (via Stroop Task, N-back, and Psychomotor Vigilance Task (PVT)), and emotional regulation. Descriptive statistics and correlation analyses were performed to investigate the relationships between sleep deprivation, cognitive performance, and emotional regulation. Results: Descriptive statistics indicated that participants reported an average of 5.81 hours of sleep per night (SD = 1.83). The average reaction time on the Stroop Task was 3.24 seconds (SD = 0.83), with N-back accuracy averaging 75.01% (SD = 13.67). The mean emotion regulation score was 38.15 (SD = 17.13). Correlation analysis revealed that sleep hours were negatively correlated with Stroop Task reaction time (r = -0.34, p Implications: This study underscores the importance of sleep-in cognitive performance and emotional regulation. The findings suggest that sleep deprivation impairs both cognitive function and emotional regulation. Integrating Artificial Intelligence (AI) and machine learning techniques could enhance our ability to predict and assess cognitive performance and emotional regulation, based on sleep data. For example, AI-powered tools could automate the analysis of cognitive task performance in real-time and develop personalized interventions to improve sleep and its impact on mental health. Future research could explore how AI technologies might be used to monitor sleep patterns and optimize cognitive and emotional well-being, offering promising avenues for individualized care and treatment in sleep-deprived populations.

6,435 views
1,969 downloads
DOI:
10.61113/impact.V2I1.1263

Contributors:

 MD. Faisal Ahmed
Research PaperID: impact-00001244Pages 29-34

AI, Media Algorithms, and Psychological Harm: Gendered Implications for Ethical Psychology and Mental Health

Patel Dhara Kaushikkumar

The swift adoption of Artificial Intelligence technologies in media production and dissemination has brought about a dramatic change in terms of psychological storytelling. This study concerns itself with the implications of AI-enhanced headline manipulation and viral algorithms that breed gender-insensitive, unethical, and psychically harmful media content, especially towards women and young audiences. Using antecedents of previous scholarly investigations and personal interactions with journalism program students over the last two years, this study points towards a reduction of factual and investigational reporting of women’s realities towards predictable and attention-driven storytelling. The results have shown that AI-assisted content platforms tend to promote selective attention, hate-based framing, victim blaming, and disregard for principles of psychological safety, gender sensitivity, and media ethics. Furthermore, it will examine in greater detail the impact of being confronted with these stories on public perceptions, the normalization of stigma, and mental health suffering. This study, which is underpinned by humanistic AI and ethical psychology paradigms, proposes that psychological ethics, gender sensitivity, and accountability systems must correlate with media practices in AI-mediated media. Also deriving from my work in You Are Not Alone and in Dhara for Dhara, this paper also underscores the importance of youth leadership education in enhancing media literacy. This paper contributes to ongoing debates about responsible uses of AI in highlighting media discourses as important vectors in ensuring that mental well-being is protected.

6,292 views
1,873 downloads
DOI:
10.61113/impact.V2I1.1264

Contributors:

 Patel Dhara Kaushikkumar
Research PaperID: impact-00001245Pages 35-53

A systematic review on the psychological implications of AI-generated visual content for user's Self-esteem and Self-perception

YAASHA LIZ VARGHESE, TENZIN SALDON, DR. KOUSALYA R

Over the past decade, the use of AI-generated visual images has grown exponentially, posing novel psychological challenges. This is particularly evident in constructs such as self-esteem and self-perception, which together influence basic psychological processes underlying identity formation, social comparison, and self-worth. In contrast to traditional media, AI-generated content provides algorithmically optimized, hyper-realistic depictions that could skew standards of self-image. This systematic review examines the relation between exposure to AI- AI-generated visual information and psychological effects spanning across diverse demographicsand contexts. To identify empirical and theoretical evidence on these psychological effects of AI-generated images, a comprehensive literature search was conducted across several databases, including Scopus, PsycINFO, PubMed, Springer, ACM, and ResearchGate. Research findings show that self-esteem is affected by different AI-generated images, leading to increased social comparison driven by algorithmically refined visuals and reduced authenticity boundaries that create skewed benchmarks. Studies also show that adolescents and populations within impoverished countries are more vulnerable to this phenomenon. Gender based inequalities are especially evident when it comes to non-consensual intimate imagery and body image issues. Existing research suggests that AI-generated content may be linked to risk factors for different eating disorders and body dysmorphia. The findings underscore the need for digital literacy initiatives, ethical frameworks, and culturally aware mental health services to address the specific challenges posed by generative AI. Future research should place greater emphasis on longitudinal approaches, the development of protective mechanisms, and strategies that minimise harm while thoughtfully utilizing the positive applications of generative AI.

6,298 views
1,942 downloads
DOI:
10.61113/impact.V2I1.1265

Contributors:

 YAASHA LIZ VARGHESE
,
 TENZIN SALDON
,
 DR. KOUSALYA R
Research PaperID: impact-00001246Pages 54-73

Cognitive Offloading in the Age of Artificial Intelligence: Psychological Implications of Excessive AI Dependence

Fayize P V

The rapid integration of artificial intelligence (AI) into daily life has transformed how individuals think, learn, decide, and solve problems. While AI technologies offer significant cognitive support by enhancing efficiency and access to information, growing concerns have emerged regarding excessive dependency on AI systems and its potential impact on core cognitive processes. This paper explores how overreliance on AI may influence human cognitive functioning, particularly attention, memory, critical thinking, problem-solving, and decision-making abilities. Drawing on recent empirical studies and theoretical perspectives in cognitive psychology and human–computer interaction, this review examines evidence suggesting that frequent delegation of cognitive tasks to AI tools may contribute to cognitive offloading, reduced mental effort, and diminished engagement of executive functions. Excessive dependency may weaken metacognitive awareness, impair deep information processing, and foster passive learning habits, especially among students and young adults. At the same time, the paper acknowledges that AI can act as a cognitive enhancer when used appropriately, supporting learning, creativity, and adaptive problem-solving. The paper adopts a narrative review methodology, synthesizing findings from peer-reviewed research published between 2018 and 2025, focusing on AI use in educational, professional, and everyday contexts. Ethical and psychological implications of AI dependency are discussed, emphasizing the need for balanced, human-centered AI use that preserves cognitive autonomy. The study highlights the importance of developing AI literacy, self-regulation skills, and guidelines for responsible AI engagement to prevent cognitive decline while maximizing technological benefits.

6,429 views
2,080 downloads
DOI:
10.61113/impact.V2I1.1266

Contributors:

 Fayize P V

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