Social and Emotional Competencies (SEC)

• Social and Emotional Competencies (SEC) are a multifaceted set of skills including emotional intelligence, empathy, teamwork, and communication that enhance interpersonal performance.
• Formal models break down SEC into measurable components, integrating them with technical skills to improve predictive performance in educational and organizational contexts.
• SEC training and computational implementations have shown practical benefits, improving engagement, conflict resolution, and adaptive interactions in both human and AI systems.

Social and Emotional Competencies (SEC) are operationally defined as a constellation of individuals’ capabilities related to understanding, regulating, and leveraging emotional and interpersonal skills within social contexts. SEC encompasses emotional intelligence, interpersonal communication, teamwork, empathy, self-regulation, leadership, and social awareness, and is increasingly recognized as integral to effective functioning in educational, professional, and computational domains.

1. Formal Models and Conceptualizations

SEC is typically modelled as a multidimensional construct. In domains such as software engineering, SEC is mathematically integrated into holistic frameworks of performance. For example, an individual's or a team’s performance ($P$) may be represented as a weighted sum of technical competencies ($TC$) and social/emotional competencies ($SEC$):

$P = \alpha \cdot TC + \beta \cdot SEC$

where $\alpha$ and $\beta$ denote context-dependent weights. SEC itself may be decomposed into sub-competencies such as emotional intelligence (EI), interpersonal skills (IS), communication (C), and teamwork (TW):

$$P = \alpha \cdot TC + \beta_1 \cdot EI + \beta_2 \cdot IS + \beta_3 \cdot C + \beta_4 \cdot TW$$

This formalization, as exemplified in software engineering contexts, clarifies SEC’s indispensable role in augmenting technical proficiency (Capretz et al., 2018).

2. SEC in Educational and Organizational Practice

SEC underpins collaborative dynamics, leadership effectiveness, and communication in both instructional and professional settings. In classroom environments, co-regulation frameworks such as InCoRe model the interactions between teacher emotions, classroom management behaviors, and affective communication (Bhuvaneshwara et al., 27 Feb 2025). Educators are trained using hybrid systems combining Operationalized Psychodynamic Diagnostics (OPD) and conflict-resolution models, often operationalized via formulas such as $I_t = \alpha \cdot L + \beta \cdot S$, where $I_t$ represents teacher intention, $L$ the quantified lead affect, and $S$ the social norm weight.

In organizational leadership, empirical analyses demonstrate high correlations (e.g., $r = 0.98$ between EI and empathy) between SEC dimensions and outcomes such as trust, motivation, and ethical conduct (Ćwiąkała et al., 8 Oct 2025). Teams led by individuals with high SEC exhibit stronger cohesion, improved conflict resolution, and enhanced motivational effectiveness, as substantiated by survey data and correlation matrices.

3. Assessment and Benchmarking of SEC

Quantitative and qualitative measurement of SEC is addressed via standardized benchmarks and psychometric models.

• EQ-Bench: Evaluates the emotional intelligence of LLMs through rating the intensity of emotions in dialogues, with scores calculated via absolute deviation from reference responses. High benchmark reliability is ensured (low coefficient of variation: $\approx 2.93\%$), and scores robustly correlate with broader intelligence metrics (e.g., $r = 0.97$ with MMLU) (Paech, 2023).
• SESI: Assesses social intelligence in LLMs, decomposing the construct into social consciousness (empathy, social cognition) and social facility (self-presentation, influence, concern). Results show a significant gap between academic and social intelligence in LLMs, with social factors such as prompt design and personality attributes impacting measured performance (Xu et al., 11 Mar 2024).
• Graduate Soft Skills Modeling: Centrality of SEC as intended outcomes in higher education is computed using bipartite network analysis and eigenvector centrality measures. Technical and leadership skills are highly central; empathy and ethical reasoning are statistically less emphasized, suggesting curriculum imbalance (García-Chitiva et al., 2023).

4. SEC in Computational Systems and Human–AI Interaction

SEC in computational systems is advanced through multimodal data processing and neural architectures. In speech emotion recognition, frameworks such as MFAS explicitly split emotional cues into textual (TEC) and speech-related components (SEC). Continuous modeling (e.g., Data2vec) for SEC extraction demonstrates enhanced robustness on valence, activation, and dominance metrics compared to quantization-based methods. SEC features are fused into model outputs via neural architecture search, using parameterized operations and trainable weightings (Sun et al., 2023).

In AI conversational agents, structured frameworks (e.g., SocialSim) simulate emotional support dialogues by integrating seeker persona realism (social disclosure) and supporter chain-of-thought reasoning (social awareness), thus operationalizing SEC in synthetic corpora and downstream chatbot performance (Chen et al., 20 Jun 2025). In AI-enabled educational games and digital learning tools, personalized interventions for populations such as children with ASD are co-designed with domain experts, leveraging fine-tuned LLMs to adapt social stories and measure skill acquisition (Lyu et al., 24 Apr 2024).

5. Empirical Outcomes and Implementation Challenges

Effectiveness of SEC training and interventions varies by context and population. In educational settings:

• Large-scale SEL interventions may face challenges, with evidence of null or counterproductive effects in high-ADHD/very disruptive populations (Chaisemartin et al., 2020).
• Targeted, adaptive approaches (e.g., mentoring networks for social capital, robot-mediated SEL, personalized story generation for ASD) yield measurable improvements in self-efficacy, emotional regulation, and engagement (e.g., $t(11) = -3.8774$, $p < 0.01$ for emotion regulation in STAR/Moxie (Hurst et al., 2020); $3\%-7\%$ self-efficacy gains in mentoring (Balaraman et al., 13 Jun 2024); Cohen’s $\kappa = 0.82$ for coding reliability in child–robot art reflection (Pu et al., 16 Sep 2024)).
• Challenges persist regarding curricular integration, teacher training, infrastructure, and standardized evaluation of SEC skills, especially in ludic environments for primary education (Oliveira et al., 19 Oct 2025).

6. Directions for Future Research and Practical Integration

The literature consistently recommends:

• Broadening curriculum and assessment frameworks to balance market-driven skills (creativity, leadership) with under-emphasized ones (empathy, ethical reasoning) (García-Chitiva et al., 2023).
• Developing computational benchmarks and transfer learning pipelines to capture nuanced SEC representations (as in Social IQa (Sap et al., 2019), EQ-Bench (Paech, 2023), AlignCap (Liang et al., 24 Oct 2024)).
• Embedding mentoring and co-regulation models into formal instruction to ensure equitable access, sustainable instrumentation, and cost-effective outcomes (Balaraman et al., 13 Jun 2024, Bhuvaneshwara et al., 27 Feb 2025).
• Leveraging hybrid AI systems and multimodal interaction (artificial companions, games, content mediation) to scaffold SEC in youth, with quantitative and qualitative measures guiding future iterative improvements (Hurst et al., 2020, Shen et al., 29 Jan 2025, Pu et al., 16 Sep 2024).

7. Implications for Policy and System Design

SEC is critical in enabling adaptive, empathetic, and ethical functioning across technical, educational, and organizational domains. The formal integration of SEC—whether via mathematical modeling, neural architectures, or programmatic design—must be contextualized to population needs, local culture, and operational constraints. Rigorously evaluated interventions, context-sensitive benchmarks, and cross-disciplinary collaborations are required to ensure that development and deployment of SEC-centric programs are empirically validated and scalable.

A plausible implication is that failure to adequately model, train, or assess SEC—particularly empathy and ethical reasoning—may undermine long-term outcomes in collaboration, leadership, and well-being, regardless of technical competencies. Balanced frameworks and ongoing empirical evaluation are therefore essential for sustainable progress in both human and AI-mediated environments.