Introduction

Once reserved for science fiction and Silicon Valley demos, artificial intelligence (AI) is now marking attendance, grading essays, and whispering answers in students' ears. In just a few short years, AI has evolved from an educational accessory to an embedded agent in everyday school life. Across classrooms, learning platforms, and assessment systems, AI technologies are being deployed to personalize instruction, automate feedback, and optimize learning pathways (Mittal et al., 2024; Pitrella et al., 2024). No longer confined to niche pilot programs, AI now plays a structural role in shaping how curricula are delivered and how learning is experienced. These advances are reshaping pedagogical norms and transforming how students interact with knowledge, authority, and even themselves. The discourse surrounding AI in education often highlights its efficiency and potential to democratize learning, but much less attention is given to its latent effects on the psychological and developmental fabric of the learner (Ali et al., 2024).

As these AI-powered systems become more affectively responsive, more socially performative, and more integrated into the rhythms of schooling, they transform from supportive tools into active shapers of the learning experience. What was once a tool has become a co-participant in the learning process that is capable of influencing students' emotional states, self-perception, and motivation (Hulus, 2025; Wang et al., 2024; Wiese et al., 2025). Their presence can influence how students perceive success, engage with struggle, and construct academic identity over time. In environments where digital agents offer constant feedback and predictive guidance, learners may internalize different conceptions of effort, mastery, and autonomy than in traditionally human-led settings (Bilquise et al., 2022; Garcia, 2025; Venter et al., 2024). These systems, in their pursuit of optimization, may inadvertently signal to students that ambiguity, failure, or slow progress is undesirable. Unfortunately, these subtle yet persistent messages risk undermining key dispositions such as persistence, curiosity, and self-regulation. Over time, such conditioning could contribute to diminished tolerance for uncertainty, reliance on machine authority, or even attenuated interpersonal empathy in peer interactions.

This shift raises profound questions about how AI may be influencing not just academic outcomes but the cognitive, emotional, and social development of students across age groups. What kind of learners, and ultimately what kind of people, are we cultivating in an AI-mediated environment? As students increasingly engage with machine agents in place of, or in addition to, human teachers and peers, they may be absorbing new models of interaction, authority, and relational dynamics. For instance, consider students who turn to an AI chatbot instead of a classmate for clarification, or who rely on automated praise rather than developing self-assessment skills. When the relational dynamics of learning are increasingly governed by algorithms, we must ask what we are gaining, and what might we be losing. Therefore, this chapter calls for a shift in focus from merely quantifying AI's instructional effectiveness to interrogating its deeper developmental consequences. It seeks to reframe the conversation around AI in education by asking not only what these systems can do, but what kind of learners and learning cultures they help create. By elevating this dimension of inquiry, we aim to foreground student well-being, identity, and emotional integrity as central concerns in the evolving relationship between AI and education. Against this backdrop, it becomes essential to examine the broader developmental consequences of pervasive AI use in educational contexts.

Main Focus of the Chapter

AI, like previous waves of technological innovation in education, presents both opportunities and unintended consequences. As AI tools become increasingly embedded in the everyday functioning of schools, they begin to shape not only pedagogical practices but also the psychological and developmental trajectories of students. While the educational benefits of AI are often emphasized, there is growing concern about its pervasive and potentially intrusive role in shaping students' behavior, cognition, and social interactions. Technologies, especially those that mediate learning and assessment, inevitably influence the ways students engage with knowledge, perceive themselves, and interact with peers and authority figures. Despite the accelerating integration of AI in educational environments, there remains a notable research gap regarding its broader developmental and psychological repercussions. Existing studies often focus on technical efficacy, learning outcomes, and teacher adoption, with far less attention to the socio-emotional and cognitive implications for students across different age groups and learning contexts. As such, there is a need for a more comprehensive understanding of how pervasive AI usage affects identity formation, motivation, attention spans, critical thinking abilities, and emotional resilience. This chapter addresses this gap by providing a critical review of the psychological and developmental implications of AI's presence in educational settings. Specifically, this chapter explores both the benefits and risks associated with widespread AI implementation in schools, with an emphasis on long-term student well-being and developmental integrity.

Methods

This chapter adopts a dual methodological approach to examine the developmental implications of AI in educational contexts. The first component of the methodology involves leveraging interdisciplinary expertise, drawing from the collective knowledge of contributing researchers across the domains of artificial intelligence, educational technology, developmental psychology, and pedagogy (Xiao et al., 2025). This collaborative, cross-disciplinary lens aligns with the principles of transdisciplinary research (Max-Neef, 2005), which emphasizes the integration of multiple knowledge systems to address complex societal challenges (e.g., the psychological and pedagogical impacts of emerging technologies). The second component is a comprehensive review of existing literature. This includes an integrative analysis of empirical studies, theoretical frameworks, and policy-oriented discussions related to AI in education. The literature review follows the guidance of Torraco (2016), who advocates for integrative reviews as a means of synthesizing diverse bodies of work to advance understanding and generate new conceptual insights. To ensure both breadth and depth, the review includes sources drawn from peer-reviewed journal articles, conference proceedings, and scholarly book chapters, with a focus on contributions from educational and psychological scholarship. This dual approach is particularly well-suited to the rapidly evolving nature of AI in education, where technological advancement often outpaces ethical and developmental understanding (Gantalao et al., 2025; Mangubat et al., 2025). By combining technical and theoretical perspectives, this methodology supports a more holistic and developmentally grounded analysis of AI's impact on learners.

Psychological and Developmental Repercussions

Learning Engagement and Inclusion

Education is about awakening minds, triggering curiosity and wonder, and making a difference (Freire, 2007). There is evidence that young people want to contribute to solving some of the wicked challenges of our times, and that this desire serves as a strong motivator for learning. Triggering criticality and creativity in students is vital for inspiring their learning, enabling meaningful connections, and supporting their positive contributions to society and the planet during volatile and unpredictable times. This process also involves the thoughtful integration of AI. The activation of minds and hearts happens when learning is authentic, purposeful, and engaging and when flexibility is built into the learning experience to stimulate growth and transformation. Critical self-reflection on one's assumptions and the reframing of one's positionality, beliefs, and actions are key (Mezirow, 1978). Therefore, questioning and (self-)criticality are key for transformative learning experiences. Slee (2018) emphasized inclusive education is grounded in the pursuit of fairness, justice, and equity. It represents both a political commitment and an educational strategy. Achieving an inclusive society requires teaching practices that accommodate all students, including those with disabilities. The structure of the curriculum, pedagogy, assessment methods, and physical learning spaces can either foster inclusion or create barriers. How can we create powerful learning opportunities for all students (UNESCO, 2020)? How can we ensure an inclusive approach that enables critical and creative engagement in learning for everyone? Diverse and varied learning and teaching approaches, including multimedia, tools, and resources, are vital to inclusive pedagogy. These approaches create fertile spaces for critical and creative self-expression, offer choice, and support flexible engagement that suits individual learners while also stretching them.

However, there is currently limited evidence that AI has had a significant impact on inclusive practice (Díaz & Nussbaum, 2024). For some time, the opportunities for flexible connection and self-expression have been abundant, thanks to participatory pedagogies, digital networked technologies, the social web, multimedia, and open learning with people, ideas, resources, and tools. Does AI turn this offer into a superabundance? Not for all and certainly not by itself. AI does not automatically democratize education. In fact, digital inequality has grown (Freeman, 2025), as access to AI remains uneven across the world (Bulathwela et al., 2024). Knowing how to harness the power of AI for learning is crucial. AI does not understand the truth or possess knowledge itself, and it struggles with complex reasoning. Its outputs are based on data, algorithms, and the prompts users provide. This is why learner criticality is essential, as well as understanding what AI can and cannot do and what responsible and ethical AI use looks like. Atenas et al. (2025) illuminated the importance of data and AI literacies. Participation and co-creation are key strategies to protect vulnerable individuals and groups and to mitigate the data asymmetries deeply rooted in existing power structures. For school students, AI literacy is particularly important, as it enables them not only to understand complex concepts but also to engage fully with AI and better prepare them for the job market and for life. Superficial use of AI can lead to the meaningless generation of outputs that mislead, miscommunicate, and hold little or no educational value. The two UNESCO AI competency frameworks, one for educators (Miao & Cukurova, 2024) and one for students (Miao et al., 2024) provide scaffolds for responsible AI integration into education that progressively develops deeper understanding and expands competencies for educators and students through the effective use and application of AI.

Support for Flexible Learning

A review of empirical and conceptual pedagogical frameworks for blended, fully online, and open settings revealed that four dimensions are particularly important when learning and teaching in technology-rich environments: choice, activity, facilitator support, and community (Nerantzi, 2018). These dimensions enable learning that is varied, flexible, and tailored to individuals' needs and aspirations. However, autonomy must be carefully balanced with appropriate scaffolding to ensure that too much choice does not become overwhelming. Bringing learners together into a community acknowledges the critical role of learning relationships and collaborative learning, both within and beyond formal courses, where diversity is valued as enrichment (OECD, 2023). Such space fosters growth and flourishing for all learners when psychological safety is present, where all voices are heard and respected, and where learning with and from each other is made possible. Learning is socially constructed, and it is through interactions, conversations, debates, and co-creation that individuals not only learn from others but also reduce power asymmetries, discover themselves, and deepen their understanding (Vygotsky, 1978). Conversation, dialogue, and co-creation remain central to the learning process. The deeper the engagement in learning, the more questions naturally emerge.

The pandemic revealed a dependency, and perhaps even over-reliance, on tutors for feedback and access to learning materials. Now, students increasingly turn to AI. Educators are not always available when learners have urgent questions, nor are peers always accessible for idea exchanges. Personalized learning supported by AI is becoming a reality. AI acts as a new learning companion that is available 24/7, ready to engage in conversation, provide feedback, and respond to unlimited queries without judgment. For some students, especially those less confident in sharing work-in-progress with others, this may be seen as a significant advantage. However, there are concerns. Could overuse and over-reliance on AI create new forms of dependency (Bozkurt et al., 2024)? Could it harm human-to-human relationships and reduce learners' comfort and confidence in dealing with interpersonal conflict, debate, and collaboration? Furthermore, if students are not critically engaging with AI-generated outputs, there is a risk of bypassing deep learning altogether, completing tasks and assessments strategically without meaningful cognitive development. Educators thus play a vital role in designing opportunities for flexible, AI-supported learning within the curriculum. This requires developing their own understanding and competencies in AI grounded in sound pedagogical principles (Díaz & Nussbaum, 2024). Educators must model the responsible and transparent use of AI, experiment thoughtfully, and design learning experiences that prioritize choice, responsibility, meaningful engagement, and assessment practices that foster agency, motivation, and a genuine desire to learn.

Feedback and Performance Monitoring

Amid the accelerating integration of AI into educational practice, few areas are as double-edged or as fundamentally transformative as feedback and performance monitoring. In line with behaviorist learning models, AI provides granular feedback that helps students identify errors and adjust their strategies in real time (Hulus, 2025). For instance, platforms such as Khanmigo now offer real-time corrective prompts during problem-solving tasks. Yet, while the promise of immediacy is substantial, the psychological risks are equally pronounced. According to the Self-Determination Theory, over-reliance on external feedback may undermine intrinsic motivation. Particularly in creative or subjective domains, instantaneous AI critiques may inadvertently stifle divergent thinking by encouraging conformity to algorithmic standards (Garcia, 2024). Thus, the pedagogical imperative is clear: AI-generated feedback must be strategically delayed or mediated in certain contexts to preserve critical self-assessment capacities and creative resilience. In terms of personalization, AI's ability to tailor feedback to individual learner profiles aligns with differentiated instruction models and universal design for learning (Sewell et al., 2022). By analyzing patterns in student work, AI systems can diagnose specific misconceptions and offer bespoke scaffolding. A case in point is adaptive learning platforms like DreamBox (Figure 1), which modulate mathematical instruction dynamically based on real-time learner performance. However, the promise of personalization is accompanied by significant equity risks. Drawing from critical algorithm studies, we must recognize that if AI systems are trained on biased or non-representative datasets, feedback could systematically disadvantage certain demographic groups (Acut et al., 2025; Bozkurt et al., 2024). In one real-world example, automated essay scoring algorithms were found to penalize essays written in non-standard dialects (Ofosu-Ampong et al., 2024). To mitigate these risks, policies must mandate algorithmic transparency, ensure diverse training datasets, and require that AI feedback mechanisms be embedded within broader dialogic feedback cultures where human educators contextualize and challenge AI outputs.

Another important point of consideration is that while AI's capacity for detailed performance monitoring enables unprecedented insights into learning trajectories (Yampolskiy, 2025), this constant surveillance simultaneously introduces psychosocial hazards. Continuous tracking can produce a "panopticon effect," where students internalize the sense of being constantly observed, leading to heightened anxiety, performance pressure, and strategic compliance rather than authentic engagement. Empirical studies have documented increased rates of academic stress in environments utilizing AI-based proctoring and monitoring tools (Seo et al., 2021), suggesting that surveillance undermines both well-being and genuine learning orientation. For example, students subjected to persistent biometric tracking during online examinations reported feelings of dehumanization and distrust toward educational institutions. Thus, any implementation of AI monitoring must adhere to strict principles of proportionality, consent, and psychological safety. AI-driven performance data should be used primarily for formative, not punitive, purposes, and students must be given an explicit agency over how their data is collected, interpreted, and used. Therefore, the deployment of AI in schools must be meticulously anchored to educational theory, critical ethics, and a commitment to preserving learner autonomy, equity, and well-being. A human-centered AI feedback ecosystem where immediacy, personalization, and tracking are used judiciously rather than ubiquitously remains essential to fostering empowered learners in the age of pervasive intelligent technologies.

Social Interaction and Collaboration

Consistent with sociocultural theories of development (Vygotsky, 1978), the integration of AI into educational settings demands a reexamination of how social interaction and collaboration are constituted, mediated, and potentially disrupted. Peer collaboration, once predominantly nurtured through direct interpersonal engagement, is increasingly mediated by intelligent systems that scaffold group dynamics, monitor participation, and algorithmically optimize group compositions (Ofosu-Ampong et al., 2024). For instance, platforms like Disco (Figure 2) can now automatically assemble student groups based on complementary skill profiles and interaction histories. While this offers a powerful mechanism for expanding learning opportunities across geographic and socioeconomic boundaries, it risks reducing the unpredictability and friction integral to authentic collaborative learning. Moreover, persistent mediation by AI interfaces attenuates the micro-social cues fundamental to socio-cognitive development, including gesture, tone, and proxemics (Sethi & Jain, 2024). Without intentional design promoting high-fidelity interaction modes (e.g., live video or virtual reality-based collaboration), students may experience "social deskilling" or the erosion of their capacity for empathy, negotiation, and non-verbal attunement (Miranda et al., 2025). A poignant example arises in international virtual classrooms, where AI translation tools remove language barriers but simultaneously strip away cultural nuance, leading to miscommunication despite linguistic clarity. Therefore, the recommendation is not mere technological enhancement but pedagogical re-engineering. AI tools must be mandated to simulate, rather than supplant, the messy, situated nature of real-world collaboration.

Turning to teacher-student interaction, the affordances of AI-driven personalization can potentially realize the vision of differentiated instruction championed by educational psychologists (Xie et al., 2023). For example, AI platforms can detect when a student's cognitive load exceeds optimal thresholds and adjusts the difficulty accordingly. Yet, critical developmental theories highlight that students' academic risk-taking and motivation are deeply contingent on relational trust and perceived emotional support from teachers. Overemphasis on AI-mediated feedback can dilute these relational anchors, fostering an instrumentalist view of learning devoid of emotional resonance. A real-world illustration is seen in AI-based homework systems, where students increasingly prefer algorithmic feedback over teacher consultation, leading to a measurable decline in classroom participation rates (Ofosu-Ampong et al., 2023). To counteract this trend, blended learning designs must deliberately preserve and even elevate the salience of human teacher-student bonds through structured "relational check-ins" and dialogical pedagogy (Zhang, 2025). In another facet, AI's role in facilitating social-emotional learning (SEL) must be approached with acute theoretical and ethical caution. While machine learning algorithms can detect emotional states through sentiment analysis or facial recognition, the epistemological basis of emotion itself poses a fundamental challenge to algorithmic interpretation. A vivid case emerges in AI-driven SEL programs in U.S. middle schools, where automated mood trackers misinterpreted cultural expressions of respect (e.g., avoiding direct eye contact) as indicators of social withdrawal. As Vygotskian perspectives emphasize, emotional regulation is co-constructed through authentic social interactions, not computational approximations (Vygotsky, 1978). Therefore, while AI can assist in early detection and resource recommendation, comprehensive SEL must remain grounded in human facilitation, narrative approaches, and culturally responsive pedagogy. Integration should prioritize scenario-based simulations, participatory role-play, and reflective discussions where AI tools act as supplements for human mentorship.

Emotional Development

As AI extends its reach into the most intimate domains of education, perhaps nowhere is its influence more ethically and psychologically fraught than in the domain of emotional development. AI-driven emotional learning systems are increasingly integrated within SEL frameworks. These tools offer interactive simulations, role-playing environments, and real-time feedback mechanisms aimed at cultivating emotional competencies (Hsu & Chaudhary, 2023; Indellicato, 2024). These interventions resonate with the model of emotional intelligence, wherein perceiving, using, understanding, and managing emotions are seen as critical for adaptive functioning. Intelligent tutoring systems (ITS) analyze emotional cues, which include but are not limited to expressions of frustration, signs of disengagement, moments of anxiety, and adjust their responses to provide personalized guidance. Researchers have found that AI-powered empathetic agents can enhance motivation and provide emotional support, fostering emotional engagement and empathy in educational settings (Jamaluddin & Mokhtar, 2025; Mayrene et al., 2024). Some argue that this kind of tailored support helps students build emotional confidence in ways traditional methods cannot (Zong & Yang, 2025). Llurba and Palau (2024) found that real-time emotion recognition software enhances students' emotional awareness by analyzing their facial expressions and engagement levels, enabling teachers to adjust instructional strategies in real time to create a more emotionally responsive learning environment. Unlike a classroom setting where a single teacher must manage the varied emotional needs of many students, AI can focus on the individual, offering support tailored to a specific emotional state.

However, while these technologies can facilitate skill acquisition within controlled environments, they fall short of replicating the emergent, reciprocal nature of authentic human emotional interaction (Vistorte et al., 2024). The ability to recognize patterns in emotional expression is different from truly understanding emotions. Artificial intelligence processes vast amounts of behavioral data and responds based on predefined models, but its reactions remain scripted, even when they appear natural. This fundamental gap between imitation and understanding raises concerns about what students are learning when they engage with AI-driven SEL tools. One of the most well-documented psychological phenomena in human-AI interaction is the ELIZA effect, named after an early chatbot designed in the 1960s. Although ELIZA functioned on simple pattern-matching algorithms, many users believed it genuinely understood them (Eisenmann et al., 2023). This tendency to ascribe human-like emotional intelligence to artificial systems persists in modern AI. Over time, it can lead to over-reliance on artificial emotional support and reduced opportunities to practice the kind of dynamic, reciprocal engagement that real-world relationships require. Developers are aware of these limitations and have taken steps to reduce the risk of AI being mistaken for a true emotional presence (Garcia et al., 2025). Some AI-driven educational programs emphasize transparency to ensure students understand they are interacting with a tool rather than a sentient entity (Raza et al., 2024). Regulatory frameworks are beginning to reflect these concerns. The European Union Artificial Intelligence Act, which began its phased implementation in February 2025, places strict controls on AI systems that influence human behavior. Emotion recognition technology is now prohibited in educational settings except for medical or safety purposes, with concerns about privacy, reliability, and the ethical implications of such systems playing a role in this decision. AI tools that assess student learning, influence admission decisions, or monitor behavior during exams are now classified as high-risk, subject to stringent transparency and accountability measures.

Autonomy and Self-Directed Learning

AI is reshaping education by enabling self-paced and personalized learning experiences. Moving away the traditional one-size-fits-all curriculum, AI analyzes student interactions to tailor content, helping learners reinforce strengths while revisiting challenging concepts with adaptive guidance (Jian, 2023). This adaptability ensures a tailored educational experience, allowing students to take ownership of their learning. The benefits of AI-driven learning extend beyond convenience; it actively cultivates a deeper, more engaged educational experience. By continuously assessing student progress and adjusting instruction accordingly, AI ensures that no student is left behind or held back by rigid curricula. AI-driven tools analyze student performance in real time, dynamically adjusting content to provide targeted instruction (Izzat Ruslim & Khalid, 2024) while delivering personalized feedback to enhance learning outcomes (Venter et al., 2024). Platforms like Knewton (Figure 3) exemplify this approach using adaptive learning technologies to tailor instruction to individual learners' needs. This real-time feedback fosters a responsive learning environment that caters to individual needs to ensure continuous improvement. By offering individualized pathways, AI fosters greater student autonomy and empowers learners to develop critical thinking and problem-solving skills without constant teacher intervention. With AI-driven learning, students are encouraged to take a more active role in their education, leading to increased motivation, engagement, and long-term learning retention. The flexibility of AI-driven learning systems also accommodates diverse learning styles and paces, making education more accessible and inclusive for students with different abilities and backgrounds.

However, the increasing autonomy afforded by AI also challenges the traditional role of educators. As students become more reliant on AI-generated feedback and support, they may decrease their engagement with teachers, shifting their perception of academic success away from human mentorship (Gupta et al., 2024). While AI enhances individualized learning, it may inadvertently weaken the student-teacher relationship, a critical component of academic and emotional development. Studies suggest that students often trust AI-generated content more than their teachers' explanations, which can weaken the student-teacher relationship (Alasgarova & Rzayev, 2024). This over-reliance on AI risks diminishing the value of human interaction in learning, potentially leading to a lack of interpersonal skills and emotional intelligence. The efficiency of AI in offering quick solutions may also reduce students' willingness to seek deeper discussions or collaborate with educators in their learning process. By prioritizing efficiency over dialogue, students may develop a surface-level understanding of subjects rather than engaging in the critical inquiry that human educators facilitate. AI-driven environments raise concerns about the erosion of human decision-making in education, potentially diminishing teachers' influence over curriculum design and student development (Ahmad et al., 2023). If AI assumes a dominant role in determining educational content, there is a risk of homogenization, where diverse perspectives and pedagogical approaches are sidelined in favor of algorithmically optimized instruction. This shift highlights the need for educators to adapt their roles, emphasizing the human aspects of learning, such as mentorship, critical thinking, and emotional intelligence, to maintain their indispensable presence in the evolving educational landscape (Gentile et al., 2023). As AI continues to shape education, it is crucial to strike a balance between technological efficiency and the irreplaceable benefits of human guidance, ensuring that AI remains a tool for enhancement rather than a substitute for human interaction in learning.

Emotional Resilience

Following the discussion on emotional development, it is crucial to address how AI intersects with the cultivation of emotional resilience, a psychological construct vital for academic success and long-term well-being. Emotional resilience, defined as the capacity to adapt to stress and recover from adversity (Grant & Kinman, 2014), is shaped by a complex interplay of internal factors, such as self-regulation and emotional intelligence, and external influences such as social support and environmental stressors. In educational settings, resilience serves as a protective factor against burnout, academic failure, and mental health challenges, particularly during periods of heightened uncertainty or transition. Effective coping strategies are central to resilience as they empower students to reframe challenges and maintain psychological equilibrium (Diaz et al., 2025). Emotional intelligence further enhances resilience by promoting self-awareness, emotional regulation, and adaptive interpersonal skills. Empirical studies corroborate that students with higher emotional intelligence exhibit more robust coping mechanisms, leading to superior academic performance and psychological health (e.g., Guo et al., 2024). In this context, AI offers powerful tools for supporting emotional resilience by facilitating personalized emotional support, real-time monitoring, and adaptive interventions (Vistorte et al., 2024). Through sophisticated pattern recognition and data analysis, AI systems can detect signs of emotional distress, track physiological markers such as heart rate variability, and provide tailored resilience-building exercises (Mishra et al., 2024). For example, wearable devices combined with AI-powered emotional coaching have shown promise in enhancing emotional regulation among graduate students by correlating biometric data with emotional states and tailoring interventions accordingly (Liu & Zoghi, 2024). Similarly, AI-driven platforms now offer virtual therapy sessions and real-time coaching that deliver cognitive-behavioral techniques and stress-management strategies at scale (Garcia, 2023; Olawade et al., 2024; Tomé & Coelho, 2023).

However, despite these benefits, significant theoretical and ethical concerns persist. AI, by its nature, lacks genuine emotional experience as its empathy remains a simulation based on probabilistic pattern matching (Xiao et al., 2025). From a phenomenological perspective, authentic emotional support is not merely the delivery of appropriate responses but emerges from embodied and reciprocal presence that AI fundamentally cannot replicate. Excessive reliance on AI for emotional support risks commodifying and dehumanizing the learning process, as educators and institutions may increasingly delegate relational responsibilities to algorithmic systems (Acut et al., 2025; Naseer et al., 2025). This detachment could lead to a diminution of the rich, trust-based human relationships essential for genuine emotional resilience. Moreover, AI's interpretation of human emotion remains vulnerable to misclassification and cultural bias. Subtle emotional cues influenced by cultural background, individual neurodiversity, or personal history may be misunderstood or flattened into reductive models of emotional expression. Such misinterpretations can exacerbate alienation rather than foster resilience. Consequently, a human-AI collaboration model is imperative (Huang et al., 2023). AI should serve as an augmentative resource, offering preliminary support, early detection, and personalized scaffolding while preserving the primacy of human-to-human emotional interaction. Educators must be trained to integrate AI tools ethically and critically, ensuring that they complement rather than replace authentic relational work. After all, Emotional resilience is cultivated through shared vulnerability, authentic dialogue, and human solidarity within educational communities.

Identity and Personal Development

Building upon prior discussions of emotional and social competencies, the intersection of AI and personal identity development warrants a critical examination. AI technologies offer novel pathways for fostering self-awareness, skill development, and lifelong learning. At the center of these innovations is the principle of personalization, allowing educational content to be dynamically adapted to individual cognitive profiles, learning styles, and career aspirations (Wang et al., 2024). For instance, AI platforms like Degreed (Figure 4) use real-time analytics to recommend targeted upskilling interventions, which enables students to align their learning trajectories with evolving labor market demands (Bösl & Achtenberg, 2024). Moreover, AI-driven reflective tools (e.g., emotionally intelligent chatbots) enable students to engage in iterative self-assessment and goal-setting processes (Bilquise et al., 2022). ITS that adapt to learning rhythms provide formative feedback that scaffolds not only knowledge acquisition but also metacognitive development (Lin et al., 2023). Students are thus empowered to construct evolving self-concepts grounded in evidence-based reflections on their strengths and areas for improvement. Generative AI extends this potential by simulating complex real-world scenarios, enhancing students' strategic planning, leadership capabilities, and decision-making skills (Yan et al., 2024; Yusuf et al., 2024). Such applications are particularly salient in higher education contexts, where identity formation intersects with professional readiness. However, as identity formation is a social and existential process, AI-mediated personal development also introduces significant conceptual and ethical tensions. The creation of digital selves, such as AI-driven avatars in the metaverse (Tabassum et al., 2025), blurs the distinction between authentic and constructed identities. Anthropomorphic avatars designed to mirror physical and behavioral realism can facilitate novel modes of social interaction (Garcia, 2025), yet they also risk commodifying personal identity and distorting authentic self-expression. The dynamic interplay between human inputs and AI outputs complicates traditional understandings of selfhood, authenticity, and representation.

Empirical findings further reveal that the psychological correlates of AI use are complex and sometimes troubling. Rodríguez-Ruiz et al. (2025) found that lower self-control and self-efficacy predict higher reliance on AI tools, while higher self-esteem inversely correlates with dependence on AI-mediated interaction. These findings suggest that while AI can support personal development, it may simultaneously entrench vulnerabilities in self-concept and autonomy, particularly among students with pre-existing psychological fragilities. Moreover, challenges related to data privacy, algorithmic bias, and identity manipulation must not be understated. Biases embedded in AI agents can reinforce stereotypes, marginalize certain groups, and compromise the inclusivity of digital learning environments (Hasanah et al., 2025; Mittal et al., 2024). Furthermore, the potential for unauthorized appropriation or alteration of personal identities necessitates robust legal and ethical protection. Without deliberate safeguards, AI systems may inadvertently erode the very authenticity they seek to empower. The over-dependence on generative AI tools for academic tasks, if left unchecked, risks stunting the development of critical 21st century skills such as creative problem-solving, analytical reasoning, and reflective judgment (Wang et al., 2024). Educational strategies must, therefore, explicitly cultivate AI resilience or the capacity to engage with AI critically and responsibly. This entails fostering digital literacy, promoting independent cognitive development, and ensuring that students retain authorship over their own learning journeys.

Future Perspectives

The accelerated integration of AI in education presents a paradoxical trajectory. It simultaneously empowers and disrupts, nurtures and alienates, and enhances and undermines. As we reflect on the psychological and developmental repercussions outlined in this chapter, it becomes evident that the future of AI in education cannot be approached as a purely technical or pedagogical challenge. It is a deeply human issue that demands foresight, ethical clarity, and inclusive design. This section aims to chart a forward-thinking agenda that embraces the benefits of AI while safeguarding the cognitive, emotional, and social development of students.

From Technology-Centric to Human-Centered AI in Schools

The future of AI in education must shift from a technology-centric model to a human-centered paradigm. The integration of AI should not be guided solely by efficiency metrics or algorithmic precision but should prioritize learner well-being, identity formation, autonomy, and emotional resilience. Initiatives like the iSTAR framework (Huang et al., 2023) emphasize the importance of designing intelligent systems that foster human-AI collaboration rather than substitution. In this future, AI will not replace teachers, mentors, or social structures but will become a silent partner that is working behind the scenes to amplify human potential.

In doing so, educational institutions must critically assess the hidden curriculum embedded in AI systems—the subtle messages about behavior, achievement, and identity that learners internalize through prolonged exposure. Future AI tools must be co-designed with students, educators, and communities, drawing on principles of participatory design and epistemic justice to ensure equity, transparency, and contextual relevance (Atenas et al., 2025).

AI Literacy and Resilience as Core Competencies

To navigate AI-rich learning environments responsibly, students must be equipped not only with AI literacy but also with AI resilience, a values-based competency that enables learners to maintain agency in the face of automation and algorithmic authority (Garcia et al., 2024; Zhao, 2025). As AI becomes more pervasive, the risk of dependency and diminished human criticality increases. Future-ready learners will require cognitive tools to question, contextualize, and reinterpret AI outputs rather than accept them as infallible.

Institutional efforts must go beyond technical training. Curricula must embed AI ethics, bias recognition, emotional intelligence, and human-machine boundary awareness as fundamental learning outcomes. The dual AI competency frameworks developed by UNESCO for educators and students (Miao & Cukurova, 2024; Miao et al., 2024; Wiese et al., 2025) offer a robust roadmap for scaling such efforts globally. National education systems should formally adopt and localize these frameworks to align with regional needs and digital access realities.

Mitigating Algorithmic Harm and Data Colonialism

One of the most pressing concerns for the future is the algorithmic harm that AI systems may perpetuate, especially when trained on data that encode societal biases, reinforce stereotypes, or marginalize vulnerable learners (Xiao et al., 2025). The challenge is compounded by data colonialism, wherein learner data is extracted, processed, and commodified by global tech entities often far removed from local accountability mechanisms.

Educational ecosystems must implement transparent data governance frameworks that ensure student data is treated as a public good, not a commercial asset. This includes mandatory explainability protocols, third-party audits, and student data ownership rights. Policies must ensure that AI tools in schools undergo rigorous ethical evaluation, especially when deployed in contexts that involve behavioral monitoring, emotional analysis, or identity shaping. Following the EU's Artificial Intelligence Act, educational systems worldwide may benefit from establishing "red lines", which are explicit prohibitions on certain high-risk applications of AI in schools.

Designing for Developmental Integrity

The next phase of AI adoption in education must respect what can be termed developmental integrity, or the protection of students' holistic growth across cognitive, emotional, social, and moral domains. This means AI must never replace authentic learning experiences that depend on ambiguity, struggle, empathy, and human connection. The future of AI in schools should be invitational and not prescriptive—designing systems that prompt exploration, curiosity, and collaborative meaning-making rather than mechanistic task completion.

Such integrity can be safeguarded by anchoring AI implementation in transformative learning theories (Mezirow, 1978) that emphasize critical reflection, perspective-taking, and consciousness development. As AI tools evolve, future educational environments must resist the temptation to over-automate (Karamuk, 2025). Instead, they should curate a hybrid space where technological augmentation and human mentorship coexist, each complementing the other in promoting deep and meaningful learning (Molenaar, 2022).

Equitable Futures and the Risk of Educational Stratification

Perhaps one of the gravest risks facing the future of AI in schools is educational stratification—a scenario where well-resourced schools benefit from advanced AI systems while underfunded institutions fall further behind. Without proactive intervention, AI could become a tool of exclusion rather than inclusion, exacerbating digital divides across socioeconomic, geographic, and linguistic lines (Bulathwela et al., 2024).

Equity-focused design must be embedded at every level—from infrastructure to pedagogy to policy. Governments and international organizations must commit to digital justice frameworks that ensure universal access to AI tools and AI education. Open-source AI platforms, localized content, multilingual interfaces, and community-based AI literacy programs will be crucial in democratizing the benefits of educational AI.

A New Social Contract for AI in Education

The road ahead demands the drafting of a new social contract between learners, educators, institutions, developers, and policymakers—a contract rooted in transparency, accountability, and care. This social contract must recognize students as not merely users of AI but as rights-bearing individuals whose mental health, identity, and agency must be protected.

In crafting this contract, we must ask:

  • What kind of citizens are we shaping through AI-enabled schooling?
  • Whose interests are embedded in the algorithms that shape learning pathways?
  • How do we ensure that AI promotes dignity, curiosity, and civic responsibility rather than conformity, surveillance, or instrumentalism?

In answering these, we must remember AI is not neutral. As emphasized in The Manifesto for Teaching and Learning in a Time of Generative AI (Bozkurt et al., 2024), the narratives and metaphors we use to frame AI matter. Whether we view it as a tool, partner, threat, or mirror, AI challenges us to rethink not only how we teach and learn but also what it means to be human in an algorithmic age. It is not a question of whether we adopt AI, but how—critically, inclusively, and with unwavering commitment to education as a deeply human endeavor.

Conclusion

The pervasive integration of AI into educational ecologies is fundamentally recalibrating the psychosocial and cognitive development trajectories of students. This review underscores that while AI-mediated instructional systems afford opportunities for hyper-personalized learning pathways and optimized academic performance, they concurrently introduce significant developmental perturbations. Chief among these developmental concerns are the implications of algorithmic surveillance, pedagogical automation, and the erosion of organic peer interaction. Unfortunately, all these issues bear consequences for identity formation, self-efficacy, and socioemotional resilience. In this evolving context, students increasingly navigate teaching and learning environments where adaptive systems modulate content delivery, scaffold attention, and mediate performance feedback. While such affordances may bolster engagement and learner persistence through tailored cognitive stimuli, they risk engendering maladaptive dependencies on extrinsic reinforcement mechanisms. This dynamic may attenuate the cultivation of metacognitive autonomy, emotional regulation, and intrinsic motivation.

The engagement with AI interlocutors necessitates that learners acquire not only digital fluency but also critical interpretive frameworks to navigate algorithmic authority, mitigate epistemic passivity, and preserve agency in data-saturated contexts. In parallel, educators must assume a translational role, one who interprets AI affordances through a developmental lens, curates hybrid pedagogical modalities that foreground relational pedagogy, and safeguards the affective dimensions of schooling. Empirical inquiry is urgently required to interrogate the longitudinal impact of AI-powered tools on developmental constructs such as identity coherence, adaptive functioning, and social-emotional connectedness across diverse learner demographics. Policymakers, in turn, must articulate regulatory architectures that embed psychological safeguards, mandate transparency in algorithmic design, and ensure that AI serves as an adjunct for human-centric educational praxis. With deliberate planning, ethical oversight, and a focus on student development, AI technologies can serve as a powerful complement to inclusive and emotionally intelligent education. Without these guiding principles, however, the risks to student well-being and developmental integrity may outweigh the intended benefits.

Key Terms and Definitions

Artificial Intelligence – It refers to machine-based systems capable of performing tasks that typically require human intelligence.

Student Development – It encompasses the cognitive, emotional, and social growth of learners, which can be shaped by sustained interactions with AI technologies in educational settings.

Emotional Resilience – The ability of students to adapt to stress and adversity, potentially supported or undermined by AI-driven emotional feedback and monitoring tools.

Identity Formation – The process through which students develop a sense of self, which may be influenced by AI systems that mediate learning experiences and performance evaluation.

Socioemotional Learning – Learning that fosters emotional intelligence, empathy, and interpersonal skills, increasingly mediated by AI tools that support emotional interactions.

Human-Centered Education – An educational philosophy prioritizing student well-being, relational dynamics, and holistic development.

Algorithmic Surveillance – The use of AI systems to monitor student behavior and performance, which raises ethical and psychological concerns related to autonomy and privacy.

Developmental Integrity – A guiding principle that emphasizes the need to preserve the full spectrum of students' developmental needs when integrating AI into educational environments