Mature students transitioning into their first year of higher education face many difficulties that affect their motivation, participation and success. Their feelings of being disconnected from their peers and from their institutions are among the key barriers to the successful completion of their courses. Encouraging online student engagement among mature students to establish social connection with their instructors and peers can reduce their isolation and enhance their ‘sense of belonging’. During the academic year 2020/2021, the Lifelong Learning Centre (LLC) at the University of Leeds in the UK, decided to pilot an online chat platform ‘Differ’ including a chatbot ‘Bo’ after it had seen a decline in the use of programme Facebook groups. To promote student engagement and monitor the Differ online communities, sixteen digital student mentors were recruited and trained. During the pilot, feedback was collected from students and student mentors. A mixed-methods approach was adopted to boost robustness through triangulation. Several forms of data collection methods were used: a mentimeter, an online survey, three focus group sessions and a semi-structured interview. This study sheds light on the different aspects of creating student-led online communities and provides recommendations on how to improve the uptake of students in the future.

Translation education (TE) demands significant effort from educators due to its labor-intensive nature. Developing computational tools powered by artificial intelligence (AI) can alleviate this burden by automating repetitive tasks, allowing instructors to focus on higher-level pedagogical aspects of translation. This integration of AI has the potential to significantly enhance the efficiency and effectiveness of translation education. The development of effective AI-based tools for TE is hampered by a lack of high-quality, comprehensive datasets tailored to this specific need, especially for Arabic. While the Saudi Learner Translation Corpus (SauLTC), a unidirectional English-to-Arabic parallel corpus, constitutes a valuable resource, its current format is inadequate for generating the parallel sentences required for a didactic translation corpus. This article proposes leveraging large language models like the Generative Pre-trained Transformer (GPT) to transform SauLTC into a parallel sentence corpus. Using cosine similarity and human evaluation, we assessed the quality of the generated parallel sentences, achieving promising results with an 85.2% similarity score using Language-agnostic BERT Sentence Embedding (LaBSE) in conjunction with GPT, outperforming other investigated embedding models. The results demonstrate the potential of AI to address critical dataset challenges in quest of effective data driven solutions to support translation education.