Benutzer:Nadine Anskeit

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.