Artificial Intelligence (AI) is rapidly transforming education, yet its integration is not a value-neutral process. In this study, AI is conceptualized as a complex socio-technical phenomenon rather than a purely technical innovation. Following the OECD (2024), AI is defined as a machine-based system capable of generating outputs (such as predictions, content, or decisions) that influence physical or virtual environments. Because these systems are trained on human data, they are inherently value-laden products that can mirror and amplify systemic biases and existing educational inequalities if not intentionally managed within rigorous policy frameworks (Varsik & Vosberg, 2024).

Therefore, national AI education policy frameworks should be viewed through the dual lenses of inclusion and equity. Inclusion is conceptualized as a continuous, proactive process “concerned with the identification and removal of barriers” to the presence, participation, and achievement of all students (Ainscow, 2020, p. 127; UNESCO, 2005). It goes beyond physical placement, placing a specific emphasis on those at risk of marginalization, exclusion, or underachievement (UNESCO, 2017). Closely linked to this is educational equity, defined as ensuring fairness so that every learner's education is considered equally important (UNESCO, 2017). Together, these principles embody a transformative commitment to social justice, ensuring that educational innovations remain accessible to everyone regardless of background or ability.

While AI-driven tools—such as intelligent tutoring systems and adaptive platforms—are praised for their potential to enhance accessibility (Melo-López et al., 2025), their adoption occurs at a critical crossroads. Without active intervention and ethical governance, AI risks maintaining normative learner profiles and marginalizing those who fall outside dominant societal assumptions (Shams, Zowghi, & Bano, 2025). Insufficient regulation may expose students to risks such as algorithmic bias, data misuse, and the widening of the digital divide (van Dijk, 2020; Ghimire & Edwards, 2024).

Despite forty years of research into AI in education, the intersection of AI with inclusion and equity remains significantly underexplored (Varsik & Vosberg, 2024). Most existing literature focuses on technological efficiency or pedagogical outcomes, while few studies critically examine how these tools are operationalized within national policy frameworks. Furthermore, while over 50 governments have published national AI strategies, research indicates that these documents rarely engage meaningfully with inclusion-related dimensions (Schiff, 2022).

This study addresses this research gap by examining how inclusion and equity are conceptualized and operationalized within the national education and AI policies of Germany, Austria, and Switzerland (the DACH region). By comparing 10 official documents, the study aims to identify how inclusion and equity principles are addressed and conceptualized, which governance mechanisms are proposed, and the differences and alignments across the DACH region. This paper provides the first comparative analysis of how inclusion and equity are addressed in AI policy documents in the DACH region. It highlights promising examples of inclusive governance while also identifying structural barriers that may hinder equitable access to AI-supported learning. Building on these insights, this paper offers targeted recommendations for future policy development, advocating not only for the integration of inclusion and equity as core principles in national AI policies but also for viewing inclusion as a collaborative, cross-border effort that leverages shared best practices and joint oversight.

This study employs a qualitative, comparative research design to examine 10 official national and subnational AI policy documents from Germany, Austria, and Switzerland (the DACH region). The document selection was guided by three criteria: (i) explicit focus on AI integration in educational settings; (ii) credible authorship from national or regional ministries or government agencies; and (iii) public availability at the time of data collection. The analytical process followed a structured, multi-stage approach to qualitative content analysis developed by Kuckartz and Rädiker (2024). All documents were analyzed in their original German by three independent coders to ensure consistency in thematic interpretation. The analysis used a structured, multi-stage coding schema that combined deductive and inductive approaches. Deductive categories were derived directly from the theoretical framework, focusing on core conceptualizations of inclusion and equity. These categories specifically addressed the identification of target groups (e.g., students with disabilities or migrant backgrounds), the inclusive potential of AI (e.g., differentiation and adaptivity), and exclusion risks such as algorithmic bias and digital divide. Additionally, the deductive framework examined systemic conditions necessary for the inclusive use of AI in schools, including infrastructure and teacher training. To ensure the material’s nuances were captured, inductive categories were generated during the coding process to incorporate emerging themes, such as AI Literacy. MAXQDA software was used to manage data, record coder decisions, and facilitate frequency counts and cross-case comparisons. Discrepancies were resolved through team consensus discussions to refine code boundaries. Finally, to contextualize the national findings within a broader normative landscape, the analysis is expanded to include internationally recognized frameworks from global and European organizations — motivated by the near-complete absence of international references in the DACH documents.These are benchmarked against official DACH strategies to identify good-practice examples and derive actionable policy recommendations, providing an evidence-based foundation for aligning AI initiatives with the Sustainable Development Goal of “inclusive and equitable high-quality education” (SDG 4). This analysis will be included during the ECER presentation.

The analysis of the selected documents reveals a consistent gap between rhetorical commitments and policies for inclusion and equity in the AI field across the DACH countries. While AI is recognized as a key driver of educational innovation, issues of inclusion and equity are often overlooked. National strategies tend to focus on technological potential, economic competitiveness, and efficiency gains. However, some differences do exist among the countries. Germany demonstrates broader engagement, particularly in addressing diverse target groups, system-level conditions, and risk awareness. Austria and Switzerland show less depth, frequently relying on vague, general statements about inclusion. Overall, policies in the region lack enforceable commitments and specific, measurable implementation goals for inclusion and equity. Therefore, the study highlights a declarative approach where inclusion is mentioned but not operationalized through binding measures or evaluation criteria. A key issue identified in the analysis is also the consistent underreporting of exclusion risks in the analyzed documents, such as linguistic barriers and algorithmic discrimination. Without a nuanced understanding of learner diversity, AI technologies might perpetuate existing inequalities rather than reduce them. To harness AI's potential as an educational tool, aligning AI governance with inclusion and equity principles requires moving beyond symbolic gestures toward tangible, cross-border efforts that make educational innovations accessible to all learners, regardless of background or ability. The study emphasizes the need for more explicit, operationalized, and accountable inclusion and equity frameworks within national AI education policies. The planned inclusion of international frameworks will further help identify leverage points for more inclusive and equitable AI-driven education policies. By examining the policy conditions under which AI is integrated, this research directly addresses the ECER conference theme, changing conditions of education, and advocates for governance frameworks that prioritize inclusion and equity principles over mere technological efficiency.

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