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A central methodological problem is the validity of empirical measurements. AI-supported writing processes result in texts whose quality can only be used to a limited extent as an indicator of actual competence gains. As Rezat and Schindler (cf. 2025, p. 4) emphasize, bias, hallucinations, or algorithmically induced stylistic smoothing can lead to apparent improvements in output being primarily attributable to technical support rather than individual writing development. Empirical designs must therefore provide for procedures that differentiate between AI effects and actual learning processes, for example through additional process data, qualitative reflections, or control groups that isolate the influence of AI.
A central methodological challenge concerns the validity of empirical measurements. AI-supported writing processes can produce text quality that reflects technical assistance rather than actual competence gains (Rezat & Schindler, 2025). Empirical designs must therefore distinguish AI effects from learning processes, for example through process data, qualitative reflections, or control-group comparisons.
Another key area of tension arises from the relationship between output and process focus. While classical writing research often assesses the quality of text products using rating procedures, the creation process, especially the interaction between writers and AI systems, often remains underexposed. Lehnen and Steinhoff (cf. 2022, p. 15f.) describe this interaction as "coactivity" between humans and machines, which requires new forms of data collection. For a valid analysis, therefore, it is not sufficient to consider finished texts: log files, prompt developments, chat histories, or accompanying self-reports can provide insights into cognitive and metacognitive processes and thus significantly broaden the perspective on writing development.
A second challenge is the insufficient integration of process-oriented perspectives. Classical writing research emphasizes product ratings, yet the interaction between writers and AI—described as human–machine “coactivity” (Lehnen & Steinhoff, 2022)—remains underexamined. Log files, prompt developments, chat histories, or self-reports are therefore necessary to capture cognitive and metacognitive processes and to complement product analyses.
Furthermore, the question arises as to the sustainability of the observed effects. The majority of studies to date have used one-off interventions. Pissarek and Wild (2018) already point to the high value of pre-/post-/follow-up designs in the context of classic text production, which empirically capture both immediate and delayed effects. This is particularly crucial in the context of AI-assisted writing, as learners often benefit from short-term relief effects, but the long-term effects on self-regulation and transfer remain unclear.
A third issue concerns the sustainability of effects. Most studies rely on one-off interventions, although pre-/post-/follow-up designs are essential for detecting both immediate and delayed learning outcomes (Pissarek & Wild, 2018). This is particularly relevant in AI-assisted writing, where short-term relief may obscure long-term implications for self-regulation and transfer.
Finally, contextual and ethical factors must be systematically incorporated into methodological planning. The lack of transparency in generative models, uncertainties regarding data protection laws, and algorithmic biases (cf. Gethmann et al., 2022, p. 155ff.) can significantly influence not only research practice but also the interpretation of empirical results. Methodologically, this means that studies should disclose technical conditions and explicitly document ethical standards. Comparable reporting standards are also necessary to ensure the replicability of studies and to reveal biases in data collection or interpretation.
Finally, empirical designs must account for ethical and technological conditions. Limited model transparency, data protection issues, and algorithmic biases (Gethmann et al., 2022) influence both research implementation and the interpretation of findings. Transparent reporting of technical parameters and ethical standards is therefore necessary to ensure comparability and replicability.

Aktuelle Version vom 14. Dezember 2025, 10:47 Uhr

A central methodological challenge concerns the validity of empirical measurements. AI-supported writing processes can produce text quality that reflects technical assistance rather than actual competence gains (Rezat & Schindler, 2025). Empirical designs must therefore distinguish AI effects from learning processes, for example through process data, qualitative reflections, or control-group comparisons. A second challenge is the insufficient integration of process-oriented perspectives. Classical writing research emphasizes product ratings, yet the interaction between writers and AI—described as human–machine “coactivity” (Lehnen & Steinhoff, 2022)—remains underexamined. Log files, prompt developments, chat histories, or self-reports are therefore necessary to capture cognitive and metacognitive processes and to complement product analyses. A third issue concerns the sustainability of effects. Most studies rely on one-off interventions, although pre-/post-/follow-up designs are essential for detecting both immediate and delayed learning outcomes (Pissarek & Wild, 2018). This is particularly relevant in AI-assisted writing, where short-term relief may obscure long-term implications for self-regulation and transfer. Finally, empirical designs must account for ethical and technological conditions. Limited model transparency, data protection issues, and algorithmic biases (Gethmann et al., 2022) influence both research implementation and the interpretation of findings. Transparent reporting of technical parameters and ethical standards is therefore necessary to ensure comparability and replicability.

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