AI-Assisted Writing in Education: Ecosystem Risks and Mitigations
- URL: http://arxiv.org/abs/2404.10281v3
- Date: Tue, 14 May 2024 10:06:44 GMT
- Title: AI-Assisted Writing in Education: Ecosystem Risks and Mitigations
- Authors: Antonette Shibani, Simon Buckingham Shum,
- Abstract summary: We draw insights from extensive research integrated with practice on a writing feedback tool over 9 years at a university.
It informs the design of educational writing support tools to be better aligned within broader contexts to balance innovation with practical impact.
- Score: 3.1723777607318295
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: While the excitement around the capabilities of technological advancements is giving rise to new AI-based writing assistants, the overarching ecosystem plays a crucial role in how they are adopted in educational practice. In this paper, we point to key ecological aspects for consideration. We draw insights from extensive research integrated with practice on a writing feedback tool over 9 years at a university, and we highlight potential risks when these are overlooked. It informs the design of educational writing support tools to be better aligned within broader contexts to balance innovation with practical impact.
Related papers
- Enhancing Instructional Quality: Leveraging Computer-Assisted Textual
Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement.
We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development.
This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
arXiv Detail & Related papers (2024-03-06T18:29:18Z) - Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
We shed light on the intersectional studies of generative AI and adaptive learning.
We argue that this union will contribute significantly to the development of the next-stage learning format in education.
arXiv Detail & Related papers (2024-02-02T23:54:51Z) - Combatting Human Trafficking in the Cyberspace: A Natural Language
Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques.
We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models.
A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
arXiv Detail & Related papers (2023-11-22T02:45:01Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - The Impact of Artificial Intelligence on the Evolution of Digital
Education: A Comparative Study of OpenAI Text Generation Tools including
ChatGPT, Bing Chat, Bard, and Ernie [0.196629787330046]
This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie.
The study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement.
However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity.
arXiv Detail & Related papers (2023-09-05T08:15:00Z) - New Era of Artificial Intelligence in Education: Towards a Sustainable
Multifaceted Revolution [2.94944680995069]
ChatGPT's high performance on standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education.
This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges.
arXiv Detail & Related papers (2023-05-12T08:22:54Z) - Beyond Summarization: Designing AI Support for Real-World Expository
Writing Tasks [28.702425557409516]
Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools.
Recent work has shown that leveraging this new technology can transform writing in many scenarios such as ideation during creative writing, editing support, and summarization.
We argue that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts.
arXiv Detail & Related papers (2023-04-05T17:47:11Z) - Vision-Centric BEV Perception: A Survey [92.98068828762833]
Vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia.
The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges.
This paper compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms.
arXiv Detail & Related papers (2022-08-04T17:53:17Z) - 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) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Artificial Intelligence Technologies in Education: Benefits, Challenges
and Strategies of Implementation [8.54335661175611]
We have identified the benefits and challenges of implementing artificial intelligence in the education sector.
We have also reviewed modern AI technologies for learners and educators, currently available on the software market.
We have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide.
arXiv Detail & Related papers (2021-02-11T11:09:41Z)
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.