I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant

• URL: http://arxiv.org/abs/2403.15396v1
• Date: Fri, 16 Feb 2024 08:10:41 GMT
• Title: I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant
• Authors: Bart Rienties, John Domingue, Subby Duttaroy, Christothea Herodotou, Felipe Tessarolo, Denise Whitelock,
• Abstract summary: This study examined the perceptions of ten online and distance learning students regarding the design of a hypothetical AI Digital Assistant (AIDA) All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students' perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalisation and accessibility, together with emotional and social support. Students' concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

Related papers

• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• AI-Tutoring in Software Engineering Education [0.7631288333466648]
  We conducted an exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis. The findings highlight advantages, such as timely feedback and scalability. However, challenges like generic responses and students' concerns about a learning progress inhibition when using the AI-Tutor were also evident.
  arXiv  Detail & Related papers  (2024-04-03T08:15:08Z)
• Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students [35.05435052195561]
  The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes. This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for assessment.
  arXiv  Detail & Related papers  (2024-01-26T14:49:29Z)
• How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineering [16.5141990552784]
  We evaluate the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in Software Engineering tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.
  arXiv  Detail & Related papers  (2023-12-18T21:38:00Z)
• Assigning AI: Seven Approaches for Students, with Prompts [0.0]
  This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student.
  arXiv  Detail & Related papers  (2023-06-13T03:36:36Z)
• Perception, performance, and detectability of conversational artificial intelligence across 32 university courses [15.642614735026106]
  We compare the performance of ChatGPT against students on 32 university-level courses. We find that ChatGPT's performance is comparable, if not superior, to that of students in many courses. We find an emerging consensus among students to use the tool, and among educators to treat this as plagiarism.
  arXiv  Detail & Related papers  (2023-05-07T10:37:51Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.