## 1 Introduction  
Since the launch of ChatGPT there is considerable enthusiasm and anticipation expressed in terms of using Generative AI (GenAI) in teaching and learning [1, 2, 3, 4, 5, 6, 7], and recent evidence suggest that many students use GenAI for education. For example, a large-scale global study with 23,218 students in over 100 countries by [PERSON] et al. [8] found that 71% of respondents had already used GPT, and of those 69% indicated that it could improve their general knowledge. Most commonly, ChatGPT as an AI digital assistant was primarily used for brainstorming (29%), summarizing (27%), and research assistance (25%) [8]. Indeed, a recent study by [PERSON] [9] showed that UK students' adoption of GenAI for assessments increased from 53% in 2024 to 88% in 2025. There is some emerging experimental evidence that GenAI can enhance student learning processes and outcomes. For example, a recent systematic literature review of 69 experimental studies (58 at university level) by [PERSON] et al. [10] found improved academic performance, better affective-motivational states, and higher-order thinking propensities of students using ChatGPT relative to not using ChatGPT. Nonetheless, there are several studies that highlight concerns about GenAI too [3, 4, 11, 12, 13]. For example, a recent study conducted at four Australian universities consisting of 8028 students indicated that while 83% of students used GenAI for their studies a majority of students were sceptical (56%) or even worried about using GenAI (47%) [12]. Similarly, in a Spanish study amongst 100 younger students, 49% raised significant concerns around GenAI [13]. Indeed, there is some emerging evidence that several groups of students are worried about what happens to their data when using a public AI digital assistant (p-AIDA) like ChatGPT [8; 14; 15], where students expressed concerns around ethical and social implications, academic integrity and misuse, data privacy and data use [12; 15]. This might be particularly pertinent for distance learners who are primarily engaging online with other learners and institutional materials. A wealth of studies have shown that distance learning students often have lower retention rates relative to face-to-face students, and may benefit from having personal and timely support and guidance [15; 16]. At the same time, distance learning institutions might be hesitant to share their body of knowledge and learning materials with GenAI companies [17]. Furthermore, in comparison to face-to-face or hybrid institutions, these distance learning institutions gather a great deal of sensitive data about the learning characteristics and engagement of their learners [16]. One possible approach to addressing these concerns is for distance learning institutions to develop their own institutional AI digital assistant (i-AIDA) within a 'walled garden' framework, and/or integrate i-AIDA with existing systems using APIs in Large Language Models (LLMs). This would allow students and educators to securely use, trust, and share their data and IP (e.g., course materials, assessments, and marking criteria), thereby potentially limiting prior concerns regarding the quality of i-AIDA responses and related privacy issues. Therefore, in this trend study, we explored the views of 315 distance learning students over three distinct studies about whether (or not) the Open University (OU) should be developing an i-AIDA as opposed to using an off-the-shelf p-AIDA. To the best of our knowledge, this is the first study that looked over a period of 11 months whether students' perspectives on an i-AIDA vs. p-AIDA might change as they gain more experience with GenAI tools like ChatGPT. Therefore, the following research questions were posited:  
RQ1: What are distance learning students' perspectives of i-AIDA relative to using a p-AIDA, and do they change over time as students gain more experience with AI?