Exploring the Use of GenAI Feedback during Design Projects

2025-09-22

This study explores the development, implementation, and initial testing of an AI reviewer tool leveraging generative AI (GenAI) to enhance learning experiences in an introduction to human-centered design (HCD) course. The tool was designed using rubric-driven prompts tailored for retrieval-augmented generation (RAG), ensuring feedback that is relevant, consistent, and actionable. To evaluate its effectiveness, the AI reviewer tool was tested in the Fall 2024 course, where it generated feedback on student design project artifacts, including documentation and presentations. Students and the instructor were later separately engaged in reflective discussions to assess the feedback’s clarity, usefulness, and impact on learning. The analysis of these reflections revealed the tool's potential to guide iterative design processes, foster deeper engagement with course concepts, and complement traditional instructional feedback. Findings will inform future implementations, highlighting the role of AI-driven feedback in enhancing educational practices and preparing students for real-world design challenges.

The Impact of AI-Driven Tools on Student Writing Development: A Case Study From The CGScholar AI Helper Project

ArXiv.org · 2025-01-14

The case study examines the impact of the CGScholar (Common Ground Scholar) AI Helper on a pilot research initiative involving the writing development of 11th-grade students in English Language Arts (ELA). CGScholar AI Helper is an evolving and innovative web-based application designed to support students in their writing tasks by providing specified AI-generated feedback. This study is one of six interventions. It involved one teacher and six students in a diverse school with low income students and explored to what extent customized AI-driven feedback can support students' writing development. The findings suggest that the implementation of AI Helper supported the development of students' writing in a number of ways. It also elicited suggestions from the teacher and students about ways of improving the still in development tool.

Platformed Learning: Reshaping Education in the Era of Learning Management Systems

2025-10-24

Abstract Platforms for computer-mediated learning have existed in various forms for more than half a century. Since the COVID pandemic, their presence in developed countries has become near-universal across all levels of education beyond early childhood. After a brief history of learning management systems, this chapter explores the political economy of learning platforms. Following that, we parse the typical learning management system in a conceptual outline, highlighting the ways in which these platforms have to date tended to fossilize anachronistic pedagogies. Then we present a counterpoint from our own research and development work, creating and evaluating in practice the CGScholar (Common Ground Scholar) learning platform, including recent work developing and evaluating Generative AI applications. We conclude with an optimistic view of possible futures for education in the era of platform learning that are both transformative and efficient.

AI and peer reviews in higher education: students’ multimodal views on benefits, differences and limitations

Technology Pedagogy and Education · 2025-03-28 · 6 citations

Since the launch of ChatGPT in November 2022, educational researchers and practitioners have sought to understand the ways in which these new generative AI technologies might influence education. This article describes one such effort. The focus of the investigation was chatbots responding from large language models to the review of open-ended student work. Specifically, the authors examined university students’ multimodal views of the benefits and limitations of AI reviews as compared to human feedback. The participants were postgraduate students in a public American university. The students’ opinions of their experiences with both types of reviews were expressed linguistically, visually and gesturally, and they were submitted to discursive and socio-semiotic analyses. The results revealed a preference for human reviews. Nevertheless, the participants also identified several benefits for AI feedback, as well as ways in which it had complemented human reviews, overwhelmingly welcoming its addition as part of their educational experience.

Assessing AI-Generated Feedback Quality: Instructor Perspectives on Fine-tuned GenAI for Higher Education Student Writing

Proceedings. · 2025-06-10

This convergent mixed-methods study investigates the integration of Generative Artificial Intelligence (GenAI) for providing formative feedback in higher education.The study explores the perspectives of eleven experienced instructors regarding GenAI-generated feedback in an online graduate program.Participants reviewed sample feedback and shared their evaluations via a numerical questionnaire, an open-ended survey, and a focus group discussion.Results indicated that instructors generally viewed GenAI feedback positively, noting its relevance, clarity, actionability, usefulness, and comprehensiveness, highlighting its effective alignment with course rubrics and comprehensive guidance.However, challenges were also noted, including redundancy and overly lengthy suggestions that could overwhelm students.The study concludes with recommendations for refining GenAI to enhance its feedback effectiveness and improve the learning experience of higher education students. Characteristics of human and AI feedback on written workExisting analyses of university instructors' formative feedback on written work (e.g., Hyland & Hyland, 2001;Pearson, 2022) have revealed that such feedback typically includes praise, criticism, and suggestions aimed at motivating students, enhancing their self-efficacy, and providing actionable guidance for improvement.These comments are grounded in specific pedagogical goals and outcomes (Connors & Lunsford, 1993;Straub, 1997).Furthermore, instructors often mitigate criticism through strategies such as hedging, using question forms, and employing paired-act patterns (where criticism is softened by praise, often within the same sentence) (Hyland & Hyland, 2001).These techniques have been shown to prevent negative emotional responses that could hinder students' ability to revise their work effectively (Pearson, 2022).Additionally, studies have revealed recurring patterns in the organization of instructor feedback.For instance, Mirador's (2000) analysis of feedback on postgraduate students' writing in the UK identified a common rhetorical structure.Feedback often begins with general impressions and a recapitulation of ideas, followed by an articulation of strengths and weaknesses, suggestions for improvement, and an overall judgment of the work.This structure, referred to as the clinching pattern, ensures that feedback is comprehensive and goal-oriented, fostering both reflection and action (Holmeier et al., 2018).In recent years, AI-generated feedback has emerged as a complement to human feedback.

Multimodality and transposition in collaborative language learning

2025-03-12 · 1 citations

Multimodality is critical in our digitally driven society, especially when learning English as a secondary language. This chapter examines ‘transposition’ in the broader scope of multimodality, highlighting the agile exchange of meanings among different modes – like text, imagery, and sound – and how this exchange transcends linguistic boundaries. Digital tools, particularly machine translation, illustrate how technology transcends traditional language constraints, propelling meaning-making into new realms. These tools act as ‘cognitive prostheses’, broadening our meaning-making and cognitive capacities, and are indispensable in educational settings, where they aid in understanding and creating complex meanings for everyday academic and technical use. This chapter explores these concepts, drawing from a research study that approaches multimodality and human–technology interaction as an integral part of the communicative repertoire of humans. The semantic prostheses of machine translation and the use of multiple modes for meaning-making are utilised in support of language learning practices. Learners in this context were encouraged to learn how to use digital tools effectively in their everyday lives and to facilitate collaborative language learning. The findings of this project demonstrate the benefits of multimodal literacy and innovative digital tools towards enhanced meaning-making increased learner engagement and motivation.

The impact of AI-driven tools on student writing development: A case study

Online Journal of Communication and Media Technologies · 2025-08-08 · 3 citations

This paper aims to examine the impact of the CGScholar (Common Ground Scholar) Artificial Intelligence (AI) Helper on a pilot research initiative involving the writing development of 11th-grade students in English Language Arts. CGScholar AI Helper is an evolving and innovative web-based application designed to support students in their writing tasks by providing specified AI-generated feedback. This is a case study and relates to one of six interventions, involving one teacher and six students in a diverse school. A qualitative thematic approach to data analysis is employed, combining data from students’ initial and reviewed writing assignments, selected focus group feedback, teacher’s post-survey, and the research team’s observations. It explores to what extent customized AI-driven feedback can support students’ writing development. The findings suggest that the implementation of AI helper supported the development of students’ writing in several ways. Therefore, researchers conclude AI can be helpful in K-12 writing considering how it is calibrated to serve the teacher’s teaching objectives. The research also elicited suggestions from the teacher and students about ways of improving the still in development tool, which it is recommended to be taken into consideration.

Multiliteracies in Education

The Encyclopedia of Applied Linguistics · 2025-01-07

Abstract After defining multiliteracies and providing a brief bibliographical background, this encyclopedia entry analyzes the socio‐linguistic context which prompted the development of this approach, the multiliteracies analysis of multimodal meaning, and pedagogical applications of multiliteracies. The entry concludes with a call for education justice supported by the precepts and practices of multiliteracies.

Generative AI Application in Higher Education Student Work

Postdigital science and education · 2024 · 6 citations

• Computer Science
• Artificial Intelligence
• Mathematics education

Generative AI as a Writing Technology: Challenges and Opportunities for School Writing

2024-01-01 · 2 citations