Learning, Media and Technology

Reframing AI ethics in education: From individual responsibilisation to shared responsibility.

The accelerating integration of generative Artificial Intelligence (AI) in education has reanimated deep tensions concerning the relationship between moral philosophy, technological governance, and the lived conditions of educational labour. Recent scholarship emphasizes that thinking ethically is not the same as doing ethically (Morley et al., 2021), and that educators are increasingly confronted with socio-technical systems whose design logics escape pedagogical oversight (Kerr et al., 2020; Green, 2021). This Special Issue interrogates the limitations of dominant ethical framings in educational technologies (with a particular focus on AI and data) and proposes a transition from individual responsibility to models of shared, systemic responsibility.

Classical ethical perspectives—deontology, consequentialism, and virtue ethics—remain insufficient for grasping AI’s entanglement with platformisation, algorithmic governance, and data extractivism. Educators are placed at the intersection of multiple contradictory demands: they are expected to act ethically within systems they neither own nor control, often under institutional cultures oriented toward techno-solutionism (Nemorin et al., 2023) and compliance with standardised checklists (Directorate-General for Education, Youth, Sport and Culture, 2022). This dynamic generates what Raffaghelli et al. (2025) have called ethical paralysis: educators experience guilt, anxiety, and ambivalence not because they lack moral commitment, but because they encounter infrastructural opacity, governance vacuums, and normative ambiguity. 

Findings from transnational research (Negru-Subtirica et al., 2025) reinforce this picture: participants expressed the impossibility of “being a hero in a broken system”, highlighting how moral burdens accumulate when institutional and technological conditions prevent ethical agency. This aligns with what in the field of moral philosophy is framed as “moral supererogation” (Heyd, 2024). Indeed, as Corbin et al.(2025) have pointed out in their analysis about the problem of assessment in the era of generative AI, whenever the teacher is confronted with the necessity of finding “solutions” to deal with such new practices, there will be tensions and dilemmas. As the authors posit, dealing with AI in assessment as a “wicked problem” helps teachers to face it as a daily situation requiring flexible, contextual responses that cannot be considered perennial. We purport that such decisions are based on an ethical and deontological background, which is part of teachers’ identities and positionalities (Hayes, 2021). 

This perspective draws on wider critical scholarship that conceives ethics as a situated practice of mediation, contextualisation, and care (Bellacasa, 2017; Chan et al., 2023). However, the impact of ethical action can be also deemed structural, relational, and collective responsibility that requires a vision of AI not as the single interface or device or model, but as a relational artifact that is entangled in normative and technological structures, institutional practices and governance, and of course, the individual as person and professional (Negru-Subtirica et al. 2025). 

Building on scholarship that advocates for an ethics of care linked to community care (Holmes et al., 2022), there is an urgent need to open a debate that moves beyond the idea of ethics as mere compliance or as the sole responsibility of the individual. To reinforce this point, ethical action must also be understood as oriented toward a future that is neither predefined nor predictable, but shaped through actors’ commitment to a shared, socially grounded vision of the good (Ross, 2023).

We must also acknowledge the differences that shape epistemic spaces and tools, generating forms of injustice—something particularly evident in the case of AI and data extractivism. Therefore, any ethical perspective or action must consider how human differences (cultural, gender-based, disability-related) are entangled in the contexts where technologies are implemented. What may be ethically acceptable in one place may be deeply problematic in another. These tensions must be recognised.

Call for contributions

Grounded in this conceptual framework, we invite submissions that examine AI ethics across three interconnected domains:

1. Normative and technological structures

At this level, ethics concerns the political economy of EdTech infrastructures: platform governance, data sovereignty, opacity, and the concentration of decision-making power. AI systems in education often operate as “black/grey boxes” (Green, 2021), governed by private actors whose interests may conflict with public educational values (Bosen et al., 2023; OECD & Education International, 2023). Possible contributions include:

• Infrastructural invisibility of algorithmic processes

• Public vs. private governance tensions

• Digital sovereignty, digital agency, and critical data governance

• Procurement practices that shape educators’ ethical choices

• Community-driven efforts to expose hidden architectures of power and create alternative EdTech infrastructures through educational activism

2. Institutional practices and governance

Institutions often translate ethical aspirations into bureaucratic obligations, perpetuating an “ethics-as-compliance” model (Morley et al., 2021). Transnational analyses (Negru-Subtirica et al., 2025) show how institutional ambiguity, lack of professional development, and top-down tool adoption intensify educators’ vulnerability. Possible contributions may explore:

• Institutional accountability and decision-making that create tensions at individual and collective levels (including workload and wellbeing)

• Training, support, and capacity-building to foster educator’s agency and generate spaces for confrontation and care

• Platform adoption politics and governance gaps that undermine shared ethical responsibility

3. Pedagogical practice and educators’professional agency

Here, ethics becomes a lived process enacted in everyday decisions about teaching, learning, assessment, supervision, and student agency. Ethics is grounded in a Bildung-oriented perspective: critical inquiry, mediation, co-design, and contextualisation (Rafaghelli et al., 2025b). Possible contributions may address:

• Teachers’ relational and emotional labour 

• Ethical tensions in students’ use of generative AI

• Pedagogies of care and socio-technical reflexivity

• Educators’ epistemic authority and credibility 

• AI as a third actor in pedagogies of care

Central question of the special issue:

What would it mean to embed AI ethics within shared—institutional, infrastructural, and collective—responsibility frameworks in education?

The aim is to move beyond “responsible use” narratives and reclaim ethics as a collective, situated, and future-oriented practice.