Full text in ERIC database. This paper explores the academic integrity considerations of students' use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.

Report, not peer reviewed, full text ERIC database. In a landscape where Generative AI is becoming increasingly influential across various sectors, its role and impact in educational settings, particularly from the viewpoint of undergraduate students, becomes essential to explore. This paper delves into the perceptions of students regarding the integration of GenAI tools like Claude AI in a college writing course. The aim is to utilize AI to enhance the processes of drafting and revising written work. This study examines how students perceive the utility and educational implications of AI in writing. Their feedback indicates a positive engagement with AI, providing critical insights into its potential and limitations and highlighting its role in enhancing the quality of writing and equipping students for an AI-driven future.

Full text in ERIC database. This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a comprehensive review of the academic literature, this paper highlights the unprecedented growth of these models and their widereaching impact across various sectors. The discussion sheds light on the complex issues and potential benefits presented by LLMs, providing a comprehensive overview of the field's current state. In the context of higher education, the paper explores the challenges and opportunities posed by LLMs. These include issues related to educational assessment, potential threats to academic integrity, privacy concerns, the propagation of misinformation, Equity, Diversity, and Inclusion (EDI) aspects, copyright concerns and inherent biases within the models. While these challenges are multifaceted and significant, the paper emphasises the availability of strategies to address them effectively and facilitate the successful adoption of LLMs in educational settings. Furthermore, the paper recognises the potential opportunities to transform higher education. It emphasises the need to update assessment policies, develop guidelines for staff and students, scaffold AI skills development, and find ways to leverage technology in the classroom. By proactively pursuing these steps, higher education institutions (HEIs) can harness the full potential of LLMs while managing their adoption responsibly. In conclusion, the paper urges HEIs to allocate appropriate resources to handle the adoption of LLMs effectively. This includes ensuring staff AI readiness and taking steps to modify their study programmes to align with the evolving educational landscape influenced by emerging technologies.

Media coverage of emerging technologies like artificial intelligence (AI) often fails to distinguish between hype and reality. This results in public misconceptions that can fuel unnecessary fears or unrealistic hopes.

To improve the public’s understanding of these tools, Aspen Digital created educational primers on the fundamentals, ran a public event on how to talk better about AI, and launched a hall of fame highlighting examples of great reporting on AI.

This latest effort focuses on helping students understand these systems by publishing open education resources for high school and junior college teachers that promote media literacy. The “Interpreting AI in the News” lesson plan teaches students how to recognize and critically interpret how writing choices impact readers’ perceptions of how AI works, what it can be used to do, and who is using it.

Aspen Digital consulted with the National Association for Media Literacy Education (NAMLE), the AI Education Project (aiEDU), as well as high school educators to develop this lesson plan so that teachers have resources to promote a more nuanced and comprehensive dialogue on AI in media literacy.

The plan includes a comprehensive set of resources: a materials list, resource links, detailed instructions, a sample presentation, conversation starters, assessment guidelines, and a vocabulary guide.

In the spirit of open access and to make this work as useful as possible to educators, we’ve also uploaded the lesson plan to the following websites: OER Commons and Merlot. We welcome educators and anyone to use, repurpose, and expand upon this work, which is made available under the Creative Commons Attribution 4.0 International License.

full text ERIC database. In this article, I present a justification for implementing AI literacy courses in higher education. I explore the ethical concerns and biases surrounding AI technologies, highlighting the importance of critical analysis and responsible use of AI. I then propose a conceptual framework, focusing on awareness, skill development, and the practical application of AI. This framework aims to foster comprehensive understanding and empower students to leverage AI's potential while mitigating risks. I also provide sample course titles and learning objectives. The suggested course format covers AI concepts, ethical considerations, bias awareness, and practical prompt engineering skills. There is a need to integrate AI literacy courses into higher education curricula, and I offer a roadmap for implementation. By equipping students with AI literacy, colleges can prepare students to navigate an AI-driven world responsibly while nurturing innovation and critical-thinking skills needed for future success.

Full text in ERIC database. Recent developments in AI technologies and the increasing accessibility of AI tools, such as ChatGPT, have raised concerns about academic integrity in higher education. Thus, this research aims to shed light on the challenges faced by English as a Second Language (ESL) lecturers in identifying AI-generated texts, and highlighting the skills and resources needed to enhance their detection capabilities. In this qualitative study, data were collected from six ESL lecturers working in a higher educational institution in Cyprus. Participants evaluated four academic essays at C1 level to determine which, if any, of the sample had been fully or partially AI-generated, and these results were then compared to results from four AI detectors. Findings reveal that participants tended to exploit a deficit model of assessment that focuses on error as an indicator of L2 writing output, with high levels of technical and grammatical accuracy and sophisticated language use as indicators of AI-generated text. Moreover, findings suggest that limited awareness of the characteristics and metrics used by ChatGPT, as well as lack of attention to the veracity of facts and references generated by ChatGPT, were features of participants' evaluations. This study identifies the growing challenges encountered by ESL lecturers and underlines the need for digital literacy training, targeted professional development, the use of advanced detection tools and a review of assessment policies and practices in relation to AI. Additionally, this study highlights the importance of reviewing and reinforcing institutional policies and practices that safeguard academic integrity and ensure quality higher education.