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Social-Emotional Learning

created 2026-08-13 · updated 2026-11-07

Social-emotional learning (SEL) — the process of developing the competencies that enable individuals to synchronize thoughts, emotions, and actions to foster positive interactions with oneself and others: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making (the CASEL framework). In AI in education, SEL is increasingly recognized as critical because the rapid integration of generative AI into learning raises questions about students' Well-Being, sociability, empathy, and trust — and because technical AI literacy alone is insufficient for navigating AI-mediated learning environments.

Questions to Consider

  • The CASEL framework lists five competencies — self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. Which of these do you think an AI tutor could support, and which do you suspect it cannot?
  • The page distinguishes social-emotional learning from emotional intelligence. Before reading on, how would you describe the difference, and why might the distinction matter for how schools approach them?
  • If technical AI literacy alone is 'insufficient' for navigating AI-mediated learning, what do you think is missing — and what does that imply for how you prepare students (or yourself)?
  • How might heavy reliance on AI reshape a student's sociability, empathy, or sense of trust — and are those changes something education should actively design for?
  • The research suggests SEL supports the relational dimensions of learning that AI must 'complement rather than replace.' Where have you seen technology strengthen a human relationship, and where has it quietly substituted for one?

Introduction

Social-emotional learning is closely related to, but distinct from, emotional intelligence (EI): SEL/SEC (social-emotional competencies) encompasses the ability to synchronize thoughts, emotions, and actions for positive interactions, while EI is an individual's capacity to process emotional information (conceptualized through ability models — reasoning and Problem Solving — or trait models — emotional dispositions and behaviors). In the AI era, SEL matters because AI can reshape learning in ways that affect students' relational and emotional development, and because educators need both technological skill and emotional intelligence to support learners effectively.

How SEL appears in the research

  • Empathy is the affective edge of SEL, and the hardest to assess. Empathy covers the evidence on teaching and measuring it: role-play, VR and LLM dialogue all raise self-report scores, observer ratings hold together, and computer-vision proxies invert on smiling. It is the page to read before treating an empathy gain as evidence that a program worked.

  • Integrating SEC into AI literacy: The narrative review by Palmquist et al. proposes an integrated framework that combines AI literacy with social-emotional competencies, arguing that technical proficiency alone is insufficient — educators and students need both technological and emotional intelligence to navigate AI-mediated learning environments, fostering personalized learning, collaboration, and ethical engagement.

  • Teachers and relational practice: Research on AI literacy frameworks and teacher-student trust emphasizes that SEL supports the relational dimensions of learning (teacher-student and student-student relationships), which AI must complement rather than replace.

  • What a pedagogical agent can and cannot carry in SEL. Across 108 blind-rated observations, a generative-AI pedagogical agent was more consistent than educators at maintaining respectful tone and routine procedural scaffolding, while educators outperformed it at guiding reflection and promoting social-emotional knowledge — supporting a division of labor, not substitution (Raave et al., 2026).

  • Well-being and AI's affective impact: Research examines how the increasing use of generative AI affects students' socio-emotional skills, well-being, sociability, and sense of trust and empathy — concerns that motivated the OECD's call for AI literacy grounded in humanistic, social, and emotional values.

  • Affective dimensions of AI: SEL connects to Affective Computing and Well-Being research, examining how AI systems can support or undermine emotional and relational learning.

  • Policy deficit in AI × SEL: A systematic review of 65 papers at the AI–SEL intersection (Tran, Liu & Nguyen 2026) finds a substantial "policy deficit": nearly three-quarters of studies state no policy implications, and the few that do often lack actor-specific guidance. The review links policy engagement to publication venue and warns of a "techno-solutionist" trap in which technical potential is foregrounded while the institutional conditions for responsible implementation remain under-specified. It proposes a "WH-question" framework (Who, What, Why, When/Where, How) to move from "implication-as-afterthought" to "implication-as-methodology," connecting AI-for-SEL innovation to educational policy and AI Governance.

  • A 43-study mapping review of GenAI and SEL. Liu et al. (2026) coded 43 studies across six SEL domains and found cognitive competencies in 24, identity in 15, social in 14 and emotion in 7, while values (4) and perspectives (3) were marginal. Nineteen affordances clustered into seven genres; 38 of 43 studies met only the lowest transparency tier. The inventory shows where the SEL evidence is thick and where ethical reporting is thin.

Potential subtopics of SEL in the AI-era research

The knowledge base's research clusters SEL into several distinct subtopics, each with its own evidence base:

Self-efficacy and confidence

A core SEL competency (self-awareness/self-management) that strongly conditions how students interact with AI. Student dependency on AI (478 Israeli HE students) found that while skill-based AI literacy dimensions were positively associated with AI dependency, both academic and AI-specific self-efficacy and effort AI Regulation in Education were negatively associated — meaning AI literacy alone does not protect against dependency; Self-Efficacy does. Cen et al. (EC-TEL 2026) found that students with lower baseline self-efficacy achieved greater learning gains regardless of practice format, and that favor toward the tutor mattered more for tutor-based practice — underscoring the value of tailoring practice to motivational profiles. Connected concepts: Self-Efficacy, Motivation, Learner Agency.

Motivation and the "AI availability" effect

Motivation intersects with SEL's responsible-decision-making and self-management. Research on AI availability and student motivation and student/teacher motivation and self-efficacy shows that the mere availability of AI can reshape students' motivational orientation and perceived effort — relevant to how AI might undermine or support intrinsic motivation. Self-efficacy research frames effort regulation as the counterweight to AI dependency.

Emotion regulation and affective support

Emotional regulation is a key SEL competency with direct learning consequences, forming a direct SEL→achievement link. Affective Computing tools like MathBuddy model student emotions to shape pedagogical responses, and AI campus well-being tools (e.g., PsychoGPT, AURA) span prevention and intervention.

Shame, guilt, and emotional responses to AI use

Emotions regulate how students make AI use visible. "Stuck in a Spiral" (19 computing students) found that shame and guilt act as social regulators of AI use, driving hiding behaviors and selective disclosure and creating cycles of reduced agency. AI anxiety can be transformed into strategic regulation of AI as a learning resource. These connect SEL's social awareness and self-management to Academic Integrity and responsible AI use.

Trust and belonging

SEL supports relational learning and social cohesion. Principled AI education and AI chatbots for collaborative learning connect to belonging and collective efficacy — the shared belief in a team's ability to accomplish tasks. The Brookings premortem emphasizes that overreliance on AI threatens social-emotional well-being, teacher-peer relationships, and student Privacy/safety — dimensions of belonging and connectedness. Teacher-student trust is central to whether AI is perceived as supportive.

There is a sharper edge to belonging here: empathy sought from AI can be penalized. Shastry and Gray (2026) had participants read about a distressed student who went to a chatbot, a friend, a therapist, or no one. In the chatbot condition participants privately reported the least willingness to offload, then used the most indifference words about that student and rated them as stranger and more desperate. Peer judgment of the AI-seeking route compounds the hiding documented above, which is why disclosure norms belong in SEL design rather than in integrity policy alone.

Sycophancy applies the same substitution with a measured dose. Across five preregistered studies (N = 3,075; 12,766 conversations), users of an AI that affirmed their views became, over three weeks, nearly as likely to seek personal advice from it as from close friends and family. The same participants reported lower satisfaction with their real-world interactions, and a majority preferred the affirming style not for its advice but because it made them feel understood (Ibrahim et al. (2026)). The harm is relational rather than emotional, which is why emotional-support features cannot be treated as cost-free additions to a course.

Persistence, mindset, and productive struggle

SEL overlaps with the effortful dimension of learning. Framing the 5% problem identifies low student persistence as a recurring challenge in educational technology, shaped by motivation/buy-in, cognitive roadblocks, resilience under challenge, and connection. Favero et al. warn that AI that substitutes for effort erodes the very capacities education builds — aligning with Metacognition and the value of productive struggle over over-reliance.

Implications for instructors and instructional design

  • Treat SEL as a first-class design goal, not an add-on. Integrate social-emotional competencies into AI Literacy curricula (per Palmquist et al.), so students learn not just how to use AI but when and why — with attention to their own emotions, effort, and relationships.
  • Design for self-efficacy and agency. Because skill-based AI literacy can increase dependency while Self-Efficacy and effort regulation protect against it, instruction should deliberately build students' confidence and self-management alongside technical skill — e.g., scaffolded practice, low-stakes successes, and prompts that require students to verify and own AI output.
  • Use AI to support, not supplant, relational learning. AI tools should complement teacher-student and student-student relationships, and educators should retain the relational role — monitoring affect and belonging — rather than delegating it entirely.
  • Emotion-aware and affect-sensitive design. Affect-aware systems can detect and respond to emotional states (anxiety, frustration, confusion), but evidence shows benefits are not universal — moderating by learner proficiency and profile — so emotional design must be tailored, not assumed.
  • Address the emotional side of AI use. Design against shame/guilt spirals and AI anxiety by normalizing discussion of AI use, fostering transparency, and reducing surveillance-based responses that erode trust and agency.
  • Support persistence and productive struggle. Scaffold rather than substitute (per Favero et al.), so AI deepens rather than bypasses effortful learning — aligning with Metacognition and productive difficulty.
  • Train the in-the-moment verbal move. SEL support tends to be framed as design or as AI detection of affect, but instructors also need what to say when a student's emotional state threatens to end engagement — treating that state as something to work with in real time rather than a precondition for learning. Parlant's practitioner guide calls these emotional micro-interventions: brief verbal acts that validate the feeling, reframe the appraisal behind it, and reconnect the struggle to professional identity, prepared through a pre-class scouting ritual and a reusable phrasebook that Generative AI can help draft for the instructor to personalize.
  • Prepare teachers' SEL competency. Educators need both technological skill and emotional intelligence (Teacher AI Competency); teacher professional development should build capacity to support students' SEL in AI-mediated settings.

Connections to learning gains and other measures

  • Self-efficacy moderates gains. Cen et al. found lower-baseline-self-efficacy students achieved the largest learning gains, and that tutor-favorability predicted gains in tutor-based practice — showing motivational profiles shape who benefits from which format.
  • Well-being and engagement as intermediate outcomes. SEL-related outcomes (motivation, Well-Being, belonging, engagement, self-efficacy) often function as mediators of downstream achievement, and AI research increasingly measures them alongside — or in some cases instead of — raw test scores.
  • Effects are conditional, not universal. Research cautions that SEL-oriented interventions may help some learners (by profile/proficiency) and not others, so claims about SEL-based learning gains should be examined for moderator effects.
  • The harm side of the ledger. The Brookings premortem cautions that AI-driven overreliance threatens social-emotional well-being, relationships, and belonging — outcomes that, if eroded, can undermine the very foundations of long-term learning and achievement.

SEL connects to AI Literacy (as a complement that makes AI literacy relational and ethical), Affective Computing and Well-Being (the affective dimensions of AI), Self-Regulated Learning (self-management and effort regulation), Self-Efficacy and Motivation (learner beliefs that moderate AI interaction), Learner Agency (protecting learner control against dependency), Ethics (responsible decision-making), Teacher AI Competency (educators' capacity to support SEL), Student Experience (well-being and belonging), and Learning Gains (the evidence that SEL supports achievement). It relates to Higher Education and K-12 as the settings where SEL-infused AI literacy is cultivated.

Common Misconceptions

"SEL and emotional intelligence are the same construct." They are related and distinct: SEL/SEC names a set of competencies — the ability to synchronise thoughts, emotions and actions for positive interaction — while emotional intelligence names an individual capacity to perceive and manage emotion. A tool can raise a learner's emotional self-awareness without developing any social-emotional competency, and a program can build collaborative competency in students whose emotional self-perception is unchanged. Because the two are measured differently, evidence for one is not evidence for the other.

"An AI tutor that responds empathetically develops social-emotional learning." Emotional responsiveness is a delivery property; SEL is a competency outcome, and the two are not automatically linked. The relational dimensions of learning are the ones this literature says AI must complement rather than replace, which makes empathy in the tool a question about what it substitutes for rather than a benefit in itself. See affective tutoring for the mechanism by which warm response can reduce the struggle that builds persistence.

"Low persistence is a motivation problem that encouragement can fix." Persistence is the recurring constraint in educational technology — the pattern the 5% problem names — and it is shaped by motivation and buy-in, cognitive roadblocks, and resilience under challenge. Encouragement addresses one of the three. A learner who stops because the problem exceeded their working memory is not short of reassurance.

"Knowing AI is available does not change how students invest effort." It can, and the effect runs the wrong way for the weakest learners: AI availability can reduce the perceived value of effortful engagement, particularly for novices. Availability alone is a motivational intervention with a negative sign, which is why SEL-adjacent design cannot treat the presence of a helpful system as neutral.

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Metadata

LevelHigher Ed
Intended audienceLearners
Learning and instructionSelf-Regulated Learning, Well-Being
Technologies and techniquesAffective Computing
Equity, ethics, and responsible useEthics
Foundations of AI in educationAI Literacy, Teacher AI Competency
SourceHistoryEditChat

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