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arxiv: 2604.04689 · v1 · submitted 2026-04-06 · 💻 cs.CY · cs.SE

Teaching Empathy in Software Engineering Education in the Age of Artificial Intelligence

Ronnie de Souza Santos , Cleyton Magalh\~aes , Giuseppe Destefanis , Mairieli Wessel , Ann Barcomb , Sherlock Licorish , Brody Stuart-Verner , Italo Santos This is my paper

Pith reviewed 2026-05-10 19:49 UTC · model grok-4.3

classification 💻 cs.CY cs.SE
keywords empathysoftware engineering educationartificial intelligenceteaching practicessocietal implicationsbiasaccessibilitystakeholder awareness
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The pith

Educators embed empathy into core software engineering activities to help students address bias and societal impacts in AI systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper examines practices that software engineering educators use to teach empathy in courses involving artificial intelligence. It identifies five categories where empathy is woven into technical tasks such as design, evaluation, and reflection instead of being presented as a standalone subject. A sympathetic reader would care because AI systems increasingly make decisions affecting diverse people, and developers need to anticipate issues like bias and accountability. The analysis of educator reports shows these embedded approaches allow students to connect technical choices directly to real-world consequences. The result is a structured set of methods for preparing future developers of AI-enabled technologies.

Core claim

Through qualitative analysis of educator-reported practices, the paper identifies five categories through which empathy is operationalized within technical software engineering coursework: societal framing of AI systems, fairness and accessibility considerations in design and evaluation, representation of diverse users, stakeholder role awareness and responsibility, and structured reflection and feedback during development processes. The findings indicate that empathy can be embedded within core development activities rather than taught as a separate topic, enabling students to reason about bias, accessibility, accountability, and the societal consequences of AI technologies.

What carries the argument

Five categories of empathy operationalization identified from qualitative analysis of educator practices, which integrate consideration of users and society into standard AI development workflows.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This integration approach could be adapted to other technical fields where developers must weigh ethical consequences of automated systems.
  • Curriculum designers might test the categories by tracking whether graduates apply them in professional AI projects over time.
  • The method suggests a way to update existing software engineering courses without requiring entirely new modules on ethics.

Load-bearing premise

Self-reported teaching practices by educators accurately reflect effective methods and can be generalized beyond the sampled participants.

What would settle it

A controlled study measuring whether students trained with these embedded practices produce AI systems with measurably fewer bias or accessibility problems in their projects than students taught empathy separately.

Figures

Figures reproduced from arXiv: 2604.04689 by Ann Barcomb, Brody Stuart-Verner, Cleyton Magalh\~aes, Giuseppe Destefanis, Italo Santos, Mairieli Wessel, Ronnie de Souza Santos, Sherlock Licorish.

Figure 1
Figure 1. Figure 1: Card Sorting Process Practices were iteratively grouped, reorganized, merged, and re￾fined during discussion until a stable structure was reached across multiple interactions. Throughout the analysis, attention was given to how participants articulated their rationale, contextual con￾straints, and intended outcomes. The interactive setting enabled observation of how shared understandings about empathy, fai… view at source ↗
read the original abstract

Empathy has been discussed as a relevant human capability in software engineering, particularly in activities that require understanding users, stakeholders, and the societal implications of technological systems. This relevance becomes more pronounced in the context of artificial intelligence, where software increasingly participates in decisions that affect diverse individuals and communities. However, limited guidance exists on how empathy can be integrated into technical software engineering education in ways that connect with the development of AI-enabled systems. This study investigates teaching practices that educators use to incorporate empathy into software engineering courses. Using qualitative analysis of educator-reported practices, we identified five categories through which empathy is operationalized within technical coursework: societal framing of AI systems, fairness and accessibility considerations in design and evaluation, representation of diverse users, stakeholder role awareness and responsibility, and structured reflection and feedback during development processes. The findings indicate that empathy can be embedded within core development activities rather than taught as a separate topic, enabling students to reason about bias, accessibility, accountability, and the societal consequences of AI technologies. These results contribute a structured view of how empathy-oriented practices can be incorporated into software engineering education to support the preparation of students who will develop AI-enabled systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports a qualitative study of software engineering educators' self-described practices for integrating empathy into technical courses, especially for AI-enabled systems. Thematic analysis of these reports yields five categories—societal framing of AI systems, fairness and accessibility in design/evaluation, representation of diverse users, stakeholder role awareness and responsibility, and structured reflection/feedback—through which empathy is said to be operationalized. The central claim is that these practices embed empathy within core development activities rather than as a separate topic, thereby enabling students to reason about bias, accessibility, accountability, and societal consequences of AI technologies.

Significance. If the reported practices can be shown to produce the claimed cognitive and attitudinal outcomes, the work supplies a concrete, practice-derived taxonomy that could guide curriculum designers seeking to address societal dimensions of AI within existing SE coursework. The emphasis on embedding rather than add-on modules is a useful framing for technical programs.

major comments (2)
  1. [Abstract and Findings] Abstract and Findings: The inference that the five categories 'enable students to reason about bias, accessibility, accountability, and the societal consequences' rests entirely on thematic coding of educator self-reports. No student outcome measures, pre/post assessments, classroom observations, or artifact analysis are described, so the enabling claim is an extrapolation rather than a demonstrated result.
  2. [Methods] Methods: The description of the qualitative analysis (sample size, recruitment, coding protocol, saturation criteria, or inter-rater reliability) is not provided at a level that allows evaluation of the robustness or generalizability of the five categories. This information is load-bearing for any claim that the categories represent effective, transferable practices.
minor comments (1)
  1. [Abstract] The abstract would benefit from a brief statement of participant numbers and analysis approach to give readers an immediate sense of the evidence base.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline revisions to improve clarity and rigor without overstating the study's scope.

read point-by-point responses

  1. Referee: [Abstract and Findings] Abstract and Findings: The inference that the five categories 'enable students to reason about bias, accessibility, accountability, and the societal consequences' rests entirely on thematic coding of educator self-reports. No student outcome measures, pre/post assessments, classroom observations, or artifact analysis are described, so the enabling claim is an extrapolation rather than a demonstrated result.

    Authors: We agree that the study relies on thematic analysis of educator self-reports rather than direct student outcome data. The original phrasing in the abstract and findings section implies a stronger causal link than the data support. We will revise the abstract, introduction, and findings to state that the reported practices are intended by educators to help students reason about these issues, framing the contribution as a practice-derived taxonomy of approaches rather than demonstrated effectiveness. This revision will be made in the next version. revision: yes

  2. Referee: [Methods] Methods: The description of the qualitative analysis (sample size, recruitment, coding protocol, saturation criteria, or inter-rater reliability) is not provided at a level that allows evaluation of the robustness or generalizability of the five categories. This information is load-bearing for any claim that the categories represent effective, transferable practices.

    Authors: The referee is correct that the current manuscript provides insufficient detail on the qualitative methods. In the revised manuscript we will expand the Methods section to report the number of participants, recruitment approach, interview or survey protocol, iterative coding process, criteria used to determine thematic saturation, and any steps taken to establish coding reliability (such as multiple coders or member checking). These additions will allow readers to assess the transferability of the five categories. revision: yes

standing simulated objections not resolved
  • The study design collected educator self-reports only; we cannot retroactively add student outcome measures, pre/post assessments, or classroom observations without conducting a new study.

Circularity Check

0 steps flagged

No circularity: purely qualitative thematic analysis with no derivations or self-referential reductions

full rationale

This paper performs a qualitative study of educator-reported practices via thematic analysis, identifying five categories of how empathy is incorporated into SE courses. There are no equations, parameters, predictions, or mathematical derivations of any kind. The central claim—that the identified practices enable reasoning about bias, accessibility, etc.—is an interpretive summary drawn directly from the coded self-reports, not a reduction by construction or a fitted input relabeled as prediction. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing steps. The derivation chain is self-contained as standard qualitative description; the absence of outcome validation is a limitation of evidence strength, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard assumptions of qualitative education research: that self-reported teaching practices can be meaningfully categorized and that such categorization provides actionable guidance for curriculum design.

axioms (1)
  • domain assumption Self-reported practices from educators reliably capture operationalizable teaching methods for empathy in technical courses.
    Invoked implicitly in the qualitative analysis of educator-reported practices to derive the five categories.

pith-pipeline@v0.9.0 · 5531 in / 1161 out tokens · 31369 ms · 2026-05-10T19:49:14.340357+00:00 · methodology

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Reference graph

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