Algorithmic Sorting in Education: Unravelling the interplay of pedagogical imaginaries and technological visions in the shaping of AI-based Edtech
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Profile
- Project leadership
- Kenneth Horvath (PHZH)
- Organisational unit
- F-BW-BDW (Bildung und Digitaler Wandel)
- Project phase
- Completed (1.10.2022 – 30.9.2026)
- Project type
- Research & development project
- Funding
- Swiss National Science Foundation
- Participating institutions
- EdTech-Collider Lausanne; Hochschule für Technik und Wirtschaft Berlin; PH Luzern; ZeMKI (Universität Bremen)
Description
One of the crucial trends in the digital transformation concerns the uses of artificial intelligence (AI). Although mostly still in a development stage, AI-based technologies have already yielded considerable debate regarding their social consequences. While proponents argue that AI-based sorting will enhance educational equity by radically personalising teaching and learning, critical scholarship argues that algorithmic black-boxes might actually reinforce rather than abolish educational disadvantages.
Whether or not AI-based technologies will ever develop into equitable and practicable pedagogical tools will depend on many choices made during their development and implementation. The problem that motivates the proposed project is that we know little about how these various decisions are made, negotiated, and justified.
Against this background, this study asks how actors from two key fields of practice – teaching and technology – envision and shape the role of algorithmic sorting in education. Although the relations, intersections, and tensions between these two fields are crucial for understanding how educational technologies function as pedagogical and social devices, they have so far hardly ever been studied in their interplay.
The project builds on French pragmatic sociology to grasp the complexity of normative considerations, sociotechnical visions, institutional settings, technologies, and everyday work situations at play in these processes. The project employs a qualitative research strategy that combines two components:
- Focus groups (“qualitative experiments”) are organised to unravel normative, pedagogical, and technological visions and assumptions that guide how actors reason about algorithmic sorting in education.
- In-depth case studies are conducted with participants from both fields to investigate how these visions and assumptions relate to everyday professional practices.
On this basis, the study promises to significantly further understanding of how AI-based tools might transform social sorting in education and will allow to identify key points to be considered for designing and using algorithmic sorting in an equitable and sustainable manner.