Generative AI and emotional intelligence support and development in higher education

The study explores whether and how Generative Artificial Intelligence (GAI) can support the development of Emotional Intelligence (EQ) in higher education, addressing skills such as self-awareness, empathy, social skills, and emotional regulation. As digital technologies permeate education and workplaces, the authors investigate if GAI—often critiqued for weakening human connection—can instead foster emotional competencies. The research focuses on perceptions of both educators and students regarding GAI’s role in EQ development within online learning environments, aiming to inform curriculum design, policy, and pedagogical practices. By including both stakeholder groups, the study seeks a comprehensive view of perceived benefits, limitations, and implementation challenges of GAI for EQ instruction and practice, with implications for ethical use, training strategies, and alignment with sociopolitical and institutional contexts.

The review (2014–2024 focus) synthesized 22 core peer-reviewed sources from Google Scholar and ProQuest on emotional intelligence, GAI, and their intersection in education, corporate, and therapeutic contexts. Foundational EQ frameworks define EQ as learnable abilities involving recognizing, managing, and using emotions for decision-making (Akers & Porter, 2003). Early affective computing (Picard, 2000) enabled emotion recognition and adaptive educational systems, supporting tailored learning experiences and reinforcing EQ as a trainable competency (Purushothaman, 2021; Wandhe, 2024).

The future of GAI and EQ highlights progression from basic recognition to adaptive interventions that can personalize feedback and provide safe practice spaces for emotional communication without social risk, potentially benefiting learners with diverse needs (Bahroun et al., 2023; Elyoseph et al., 2024; Licardo & Lipovec, 2024; Mantulenko et al., 2024; Sethi & Jain, 2024). However, authenticity concerns persist, particularly regarding empathy and deep emotional connection, and the risk that AI cannot replicate complex, unpredictable human interactions (Rostami & Navabinejad, 2023; Bozdağ, 2024). Corporate applications show promise for training active listening and empathy (Hammad, n.d.).

The Technology Acceptance Model (TAM) frames adoption of GAI for EQ by emphasizing perceived usefulness and ease of use as primary drivers, with extended models adding social influence and facilitating conditions (Davis, 1989; Alenezi, 2024; Al-kfairy, 2024). Institutional support, infrastructure, professional development, and cultural attitudes toward technology-mediated emotional learning shape acceptance (Lawless & Pellegrino, 2007; Donkor, 2013). Overall, opportunities exist for GAI to complement EQ development, but long-term impacts and ethical boundaries require further study (Nadeem, 2024; Vistorte et al., 2024).

Design: Non-experimental quantitative study with supplemental qualitative thematic analysis. A survey with six 5-point Likert items (1=Strongly Disagree to 5=Strongly Agree) and three open-ended questions was administered via Microsoft Forms. Data were cleaned and analyzed in Excel.

Participants and recruitment: After IRB approval (45 CFR 46), current faculty and students from Purdue Global’s School of Business and IT were recruited via institutional email lists. Surveys to faculty were distributed through the Center for Teaching and Learning (CTL); student surveys via the Assistant Dean of Students (ADOS). Participation was voluntary; those not meeting inclusion criteria were excluded.

Data collection and privacy: The survey link was emailed; informed consent described background, use of data, and withdrawal. No IP addresses or demographic data were collected to ensure anonymity. Data collection spanned two weeks and were stored in Microsoft Vault with dual-factor authentication for five years per IRB requirements.

Analysis: Quantitative analysis used cumulative counts, medians, percentages, and visualizations in Excel. Qualitative open-ended responses underwent thematic analysis to identify perceived supports, benefits, and challenges of GAI for EQ development. Research questions addressed educator and student perceptions, attitudes/expectations, and perceived opportunities/barriers to GAI-based EQ development.

Sample and response: 18,974 emails sent; 568 responses received; 5 excluded; final n=563 (48 faculty, 515 students; 9% faculty, 91% students).

Central tendency: Median response was 4 (Agree) for all six Likert items, indicating at least half of respondents agreed or strongly agreed that GAI can: address unique EQ needs; aid EQ skill development; improve communication; enhance academic performance; support diverse learning/communication styles; and be incorporated into online assignments to support emotionally intelligent learning.

Support for integrating GAI for EQ (RQ1): 52% of faculty and 54% of students agreed/strongly agreed that GAI should be incorporated into online learning to support EQ; 31% of faculty and 20% of students disagreed; 17% of faculty and 26% of students were neutral.

Perceived impact on academic performance: 65% of educators and 78% of students agreed that GAI can enhance academic performance and learning outcomes; 23% of faculty and 9% of students disagreed.

Perceived impact on EQ skills (RQ2): 50% of faculty and 51% of students agreed that GAI can help develop EQ skills (self-awareness, empathy, adaptability); 38% of faculty and 28% of students disagreed; 12% of faculty and 21% of students were neutral.

Support for diverse learning and communication styles: 63% of faculty and 76% of students agreed that GAI can assist diverse learning preferences and communication styles; 21% of faculty and 11% of students disagreed; 17% of faculty and 13% of students were neutral.

GAI tool usage (RQ3): ChatGPT was most frequently reported (35%), followed by Copilot, Gemini, Grammarly, and Google; 14% reported no GAI use.

Thematic analysis (open-ended):

• Supports: Assistance with writing etiquette, clarity, tone; help with conflict resolution and reframing; breaking down complex situations for problem-solving; safe space for exploring communication (notably beneficial for neurodiverse learners).
• Benefits: Improved academic/professional communication; increased confidence and reduced anxiety; enhanced professionalism in emails/reports.
• Challenges/concerns: Risk of over-reliance reducing critical thinking; potential for generalized, incorrect, or biased outputs; need for training, clear guidelines, and ethical boundaries to distinguish tool support from substituting human thinking.

Findings across educator and student groups indicate generally positive attitudes toward GAI as a supplementary tool for EQ development, addressing RQ1 and RQ2 by showing perceived usefulness for communication, conflict resolution, and support of diverse learning and communication styles. Median agreement across all items suggests broad acceptance aligned with TAM constructs (perceived usefulness/ease of use). Higher student agreement on academic performance and learning supports may reflect stronger end-user receptivity, while faculty show more caution, reflecting concerns about pedagogy and academic integrity.

The thematic analysis clarifies opportunities (enhanced clarity/tone, confidence, problem-solving) and barriers (over-reliance, quality/bias issues, and implementation/training needs), directly informing RQ3. These results support integrating GAI within structured learning objectives, with ethical guidelines and training to maintain human-centered interaction and preserve critical thinking. Overall, GAI appears most effective as an augmentative tool complementing traditional EQ instruction rather than replacing interpersonal learning experiences, particularly for neurodiverse and differently-abled learners who may benefit from personalized, low-stakes practice environments.

The study suggests that GAI can effectively support emotional intelligence development in higher education, especially for enhancing communication, conflict resolution, and personalized learning experiences. Both faculty and students generally favor integration of GAI as a supplementary tool. Successful implementation, however, requires clear ethical guidelines, proper training for educators and students, and safeguards against over-reliance to preserve critical thinking and authentic human interaction. As GAI capabilities advance, institutions should adopt balanced approaches that leverage GAI to augment, not replace, human-centered EQ development, thereby preparing students for academic and professional environments that increasingly value emotional competence alongside technical skills.

Generalizability is limited due to sampling from a single institution’s School of Business and Information Technology. Voluntary participation may introduce self-selection bias. Participants’ emotional states during survey completion were not controlled and could influence responses. No demographic data were collected, limiting subgroup analyses. The study relied on self-reported perceptions, and the cross-sectional design precludes causal inference.