AI-generated content is rapidly becoming a salient component of online information ecosystems, yet its influence on public trust and epistemic judgments remains poorly understood. We present a large-scale mixed-design experiment (N = 1,000) investigating how AI-generated credibility scores affect user perception of political news. Our results reveal that AI feedback significantly moderates partisan bias and institutional distrust, surpassing traditional engagement signals such as likes and shares. These findings demonstrate the persuasive power of generative AI and suggest a need for design strategies that balance epistemic influence with user autonomy.

Despite the well-established importance of feedback in education, the application of Artificial Intelligence (AI)-generated feedback, particularly from language models like ChatGPT, remains understudied in translation education. This study investigates the engagement of master's students in translation with ChatGPT-generated feedback during their revision process. A mixed-methods approach, combining a translation-and-revision experiment with quantitative and qualitative analyses, was employed to examine the feedback, translations pre-and post-revision, the revision process, and student reflections. The results reveal complex interrelations among cognitive, affective, and behavioural dimensions influencing students' engagement with AI feedback and their subsequent revisions. Specifically, the findings indicate that students invested considerable cognitive effort in the revision process, despite finding the feedback comprehensible. Additionally, they exhibited moderate affective satisfaction with the feedback model. Behaviourally, their actions were largely influenced by cognitive and affective factors, although some inconsistencies were observed. This research provides novel insights into the potential applications of AI-generated feedback in translation teachingand opens avenues for further investigation into the integration of AI tools in language teaching settings.

Delivering personalised, formative feedback to multiple problem-based learning groups in a short time period can be almost impossible. We employed ChatGPT to provide personalised formative feedback in a one-hour Zoom break-out room activity that taught practicing health professionals how to formulate evaluation plans for digital health initiatives. Learners completed an evaluation survey that included Likert scales and open-ended questions that were analysed. Half of the 44 survey respondents had never used ChatGPT before. Overall, respondents found the feedback favourable, described a wide range of group dynamics, and had adaptive responses to the feedback, yet only three groups used the feedback loop to improve their evaluation plans. Future educators can learn from our experience including engineering prompts, providing instructions on how to use ChatGPT, and scaffolding optimal group interactions with ChatGPT. Future researchers should explore the influence of ChatGPT on group dynamics and derive design principles for the use of ChatGPT in collaborative learning.

Since its launch in November 2022, ChatGPT has had a transformative effect on education where students are using it to help with homework assignments and teachers are actively employing it in their teaching practices. This includes using ChatGPT as a tool for writing teachers to grade and generate feedback on students' essays. In this study, we evaluated the quality of the feedback generated by ChatGPT regarding the coherence and cohesion of the essays written by English Language Learners (ELLs) students. We selected 50 argumentative essays and generated feedback on coherence and cohesion using the ELLIPSE rubric. During the feedback evaluation, we used a two-step approach: first, each sentence in the feedback was classified into subtypes based on its function (e.g., positive reinforcement, problem statement). Next, we evaluated its accuracy and usability according to these types. Both the analysis of feedback types and the evaluation of accuracy and usability revealed that most feedback sentences were highly abstract and generic, failing to provide concrete suggestions for improvement. The accuracy in detecting major problems, such as repetitive ideas and the inaccurate use of cohesive devices, depended on superficial linguistic features and was often incorrect. In conclusion, ChatGPT, without specific training for the feedback generation task, does not offer effective feedback on ELL students' coherence and cohesion.