Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineering

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

TL;DR

This paper investigates the socio-emotional gap in HAIC for software engineering by interviewing 10 technically proficient practitioners. It shifts from replicating human SEI to a functional equivalents framework that maps ESCI-U traits to AI capabilities (internal cognition, contextual intelligence, adaptive learning, collaborative intelligence) to achieve similar collaborative outcomes. Findings indicate practitioners view AI as a cognitive tool with valuable technical contributions but lacking sustained, context-aware social collaboration, underscoring the need for functional, explainable, and trustworthy AI design. The work offers a practical design direction for HAIC in SE, highlighting domain-specific requirements and outlining future research on adaptation to task, context, and user expertise.

Abstract

As GenAI models are adopted to support software engineers and their development teams, understanding effective human-AI collaboration (HAIC) is increasingly important. Socio-emotional intelligence (SEI) enhances collaboration among human teammates, but its role in HAIC remains unclear. Current AI systems lack SEI capabilities that humans bring to teamwork, creating a potential gap in collaborative dynamics. In this study, we investigate how software practitioners perceive the socio-emotional gap in HAIC and what capabilities AI systems require for effective collaboration. Through semi-structured interviews with 10 practitioners, we examine how they think about collaborating with human versus AI teammates, focusing on their SEI expectations and the AI capabilities they envision. Results indicate that practitioners currently view AI models as intellectual teammates rather than social partners and expect fewer SEI attributes from them than from human teammates. However, they see the socio-emotional gap not as AIs failure to exhibit SEI traits, but as a functional gap in collaborative capabilities (AIs inability to negotiate responsibilities, adapt contextually, or maintain sustained partnerships). We introduce the concept of functional equivalents: technical capabilities (internal cognition, contextual intelligence, adaptive learning, and collaborative intelligence) that achieve collaborative outcomes comparable to human SEI attributes. Our findings suggest that effective collaboration with AI for SE tasks may benefit from functional design rather than replicating human SEI traits for SE tasks, thereby redefining collaboration as functional alignment.

Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineering

TL;DR

This paper investigates the socio-emotional gap in HAIC for software engineering by interviewing 10 technically proficient practitioners. It shifts from replicating human SEI to a functional equivalents framework that maps ESCI-U traits to AI capabilities (internal cognition, contextual intelligence, adaptive learning, collaborative intelligence) to achieve similar collaborative outcomes. Findings indicate practitioners view AI as a cognitive tool with valuable technical contributions but lacking sustained, context-aware social collaboration, underscoring the need for functional, explainable, and trustworthy AI design. The work offers a practical design direction for HAIC in SE, highlighting domain-specific requirements and outlining future research on adaptation to task, context, and user expertise.

Abstract

As GenAI models are adopted to support software engineers and their development teams, understanding effective human-AI collaboration (HAIC) is increasingly important. Socio-emotional intelligence (SEI) enhances collaboration among human teammates, but its role in HAIC remains unclear. Current AI systems lack SEI capabilities that humans bring to teamwork, creating a potential gap in collaborative dynamics. In this study, we investigate how software practitioners perceive the socio-emotional gap in HAIC and what capabilities AI systems require for effective collaboration. Through semi-structured interviews with 10 practitioners, we examine how they think about collaborating with human versus AI teammates, focusing on their SEI expectations and the AI capabilities they envision. Results indicate that practitioners currently view AI models as intellectual teammates rather than social partners and expect fewer SEI attributes from them than from human teammates. However, they see the socio-emotional gap not as AIs failure to exhibit SEI traits, but as a functional gap in collaborative capabilities (AIs inability to negotiate responsibilities, adapt contextually, or maintain sustained partnerships). We introduce the concept of functional equivalents: technical capabilities (internal cognition, contextual intelligence, adaptive learning, and collaborative intelligence) that achieve collaborative outcomes comparable to human SEI attributes. Our findings suggest that effective collaboration with AI for SE tasks may benefit from functional design rather than replicating human SEI traits for SE tasks, thereby redefining collaboration as functional alignment.
Paper Structure (10 sections, 3 figures, 1 table)

This paper contains 10 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Research Methodology
  4. Results and Findings
  5. RQ1: Practitioners' perceptions of the socio-emotional gap
  6. RQ2: Evolution of HAIC Capabilities
  7. Implications from results: Functional equivalents framework for effective HAIC
  8. Validation of Functional Equivalents
  9. Discussion
  10. Conclusion and Future Work

Figures (3)

  • Figure 1: Thematic analysis process showing final theme development using Braun and Clarke’s six-phase approach braun2006using.
  • Figure 2: Thematic map showing the seven themes addressing practitioners' perceptions of the socio-emotional gap in HAIC (RQ1: Themes 1–5) and desired evolution of AI capabilities (RQ2: Themes 6–7).
  • Figure 3: Functional equivalents framework: Conceptualization of SEI traits for effective HAIC as AI technical capabilities that deliver the same collaborative outcomes as human SEI traits through distinct technical mechanisms