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.

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.

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.

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.

Integrating Dependency Analysis Through Structural Equation Modeling and Artificial Neural Networks: A Case Study in the Mining Industry

2025-01-01

Generative AI in K-12 Education: The CyberScholar Initiative

ArXiv.org · 2025-01-28 · 1 citations

This paper focuses on the piloting of CyberScholar, a Generative AI assistant tool that aims to provide formative feedback on writing in K-12 contexts. Specifically, this study explores how students worked with CyberScholar in diverse subject areas, including English Language Arts, Social Studies, and Modern World History classes in Grades 7, 8, 10, and 11 in three schools in the Midwest and one in the Northwest of the United States. This paper focuses on CyberScholar's potential to support K-12 students' writing in diverse subject areas requiring written assignments. Data were collected through implementation observations, surveys, and interviews by participating 121 students and 4 teachers. Thematic qualitative analysis revealed that the feedback tool was perceived as a valuable tool for supporting student writing through detailed feedback, enhanced interactivity, and alignment with rubric criteria. Students appreciated the tool's guidance in refining their writing. For the students, the assistant tool suggests restructuring feedback as a dynamic, dialogic process rather than a static evaluation, a shift that aligns with the cyber-social learning idea, self-regulation, and metacognition. For the teaching side, the findings indicate a shift in teachers' roles, from serving primarily as evaluators to guiding AI feedback processes that foster better student writing and critical thinking.

Reducing and Optimizing Well Cost and Time by Implementing a Technical Collaborative Partnership in Roncador, an Ultra Deepwater Brownfield in Brazil

SPE/IADC International Drilling Conference and Exhibition · 2025-02-25

Roncador is an ultra-deepwater field, ranging from 1400 to 1900 m water depth, located at Northern area of the Brazilian Campos Basin, about 125 km from the coast (Figure 1). The field was discovered by Petrobras in 1996 and first oil was in 2002. Over 22 years more than 150 wells have been completed and in 22 years with peak production of 400 kboe/day in 2009 and 2015. Accumulated production is more than 1.7 Bboe. The field is currently producing from four production units (Figure 2) and started to face production decline. In 2017 Equinor and Petrobras formed a strategic partnership were Equinor acquired a 25% stake. Petrobras got 75% stake. The STAA - Strategical Technical Alliance Agreement project aims to maximize value creation and the longevity of the Roncador field utilizing the best of technology, competence, and experience from Petrobras and Equinor in a collaboration. As part of the STAA, both operators have been working side-by-side to improve efficiency and reduce well cost. Working with simplification, standardization and focusing on operational efficiency it was possible to reduce the construction duration by more than 50% and the well construction cost by more than 60%.

Letramento na Era da Inteligência Artificial

Cadernos de Letras da UFF · 2024-12-30

A mais recente iteração da Inteligência Artificial, conhecida como "IA Generativa", constitui, sobretudo, uma tecnologia voltada para o processo de escrita. A IA Generativa é uma máquina capaz de produzir textos de gêneros diversos. No contexto histórico global, a importância dessa tecnologia não pode ser subestimada, uma vez que a linguagem artificial do código se entrelaça com a linguagem natural da vida cotidiana. A capacidade de escrita da IA generativa é multimodal, indo além da produção de texto predominantemente verbal ao também "ler" e "escrever" imagens por meio de rótulos textuais e prompts. Além disso, essa forma peculiar de escrita abarca conceitos matemáticos, procedimentos de software e algoritmos. Com base nessas considerações, este artigo investiga as implicações da IA Generativa no ensino e aprendizado da alfabetização e do letramento. Inicialmente, exploramos os desafios e impactos da IA Generativa nas práticas de letramento. Em seguida, apresentamos um estudo de caso que exemplifica a implementação prática da IA Generativa no suporte ao letramento e à aprendizagem. Por fim, delineamos implicações amplas para o ensino e a aprendizagem, propondo uma nova agenda para o letramento na era da IA Generativa. Palavras-chave: Inteligência Artificial; IA Generativa; Letramento; Ensino e aprendizagem

Generative AI Application in Higher Education Student Work

Postdigital science and education · 2024 · 6 citations

• Computer Science
• Artificial Intelligence
• Mathematics education

Combining human and artificial intelligence for enhanced AI literacy in higher education

Computers and Education Open · 2024 · 122 citations

• Artificial Intelligence
• Computer Science
• Artificial Intelligence

This paper seeks to contribute to the emergent literature on Artificial Intelligence (AI) literacy in higher education. Specifically, this convergent, mixed methods case study explores the impact of employing Generative AI (GenAI) tools and cyber-social teaching methods on the development of higher education students’ AI literacy. Three 8-week courses on advanced digital technologies for education in a graduate program in the College of Education at a mid-western US university served as the study sites. Data were based on 37 participants’ experiences with two different types of GenAI tools–a GenAI reviewer and GenAI image generator platforms. The application of the GenAI review tool relied on precision fine-tuning and transparency in AI-human interactions, while the AI image generation tools facilitated the participants’ reflection on their learning experiences and AI's role in education. Students’ interaction with both tools was designed to foster their learning regarding GenAI's strengths and limitations, and their responsible application in educational contexts. The findings revealed that the participants appeared to feel more comfortable using GenAI tools after their course experiences. The results also point to the students’ enhanced ability to understand and critically assess the value of AI applications in education. This study contributes to existing work on AI in higher education by introducing a novel pedagogical approach for AI literacy development showcasing the synergy between humans and artificial intelligence.