Sabine Seufert

Due to recent advances in natural language processing (NLP), a new generation of digital learning support systems is emerging, which make it possible to analyse the writing quality of texts offering individual, linguistic feedback to writers through various kinds of automated text evaluation. These intelligent tutoring systems (ITS) have to be integrated into existing teaching practices alongside traditional feedback providers (e.g., tutor, peer students). Therefore, this paper explores how academic writing skills of students could be fostered by providing different types of feedback from a tutor, peer students and an ITS. It proposes a feedback model for academic writing in an AI-enabled learning support environment and illustrates the importance of the different feedback providers in an academic writing use case. Through this, the paper aims to contribute to a better understanding of the changing nature of how students' academic writing skills can be fostered in the age of artificial intelligence.

The digital transformation has implications for how and what to teach. For the purpose of professional development, the paper at hand presents a conceptual framework for predicting the use of technology as a means and as a content of instruction. It is informed by the TPACK framework and the ‘will, skill, tool’ model. The predictors are Technological Knowledge (TK), Technological Pedagogical Knowledge (TPK), Technological Pedagogical Content Knowledge (TPACK), Technological Collaboration Knowledge (TCoK), and Attitudes. These constructs are measured by newly developed self-assessment instruments. Structural equation modeling using a sample of 212 in-service teachers from commercial schools in German-speaking Switzerland lend support to the soundness of the measurement instrument and the conceptual framework. Overall, 36% of the variance of the use of technology as a means and 45% of the variance for the use as the content of instruction can be explained. Mediation and multigroup analyses, a finite-mixture segmentation, comparisons of competing models, and factor score regression yielded evidence for the robustness of the conceptual framework.