Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for AI-generated text and evaluates them based on accuracy and error type analysis. Specifically, the study seeks to answer research questions about whether existing detection tools can reliably differentiate between human-written text and ChatGPT-generated text, and whether machine translation and content obfuscation techniques affect the detection of AI-generated text. The research covers 12 publicly available tools and two commercial systems (Turnitin and PlagiarismCheck) that are widely used in the academic setting. The researchers conclude that the available detection tools are neither accurate nor reliable and have a main bias towards classifying the output as human-written rather than detecting AI-generated text. Furthermore, content obfuscation techniques significantly worsen the performance of tools. The study makes several significant contributions. First, it summarises up-to-date similar scientific and non-scientific efforts in the field. Second, it presents the result of one of the most comprehensive tests conducted so far, based on a rigorous research methodology, an original document set, and a broad coverage of tools. Third, it discusses the implications and drawbacks of using detection tools for AI-generated text in academic settings.

For some students, the technology can effectively replace collaborative learning with classmates and office hours with professors.

Learn 5 practical steps to update college assignments for the AI age—boosting critical thinking, authentic learning, and academic integrity.

AI offers a surprising lens on how we think about student motivation and why behavior may matter more than belief.

AI systems are no longer just specialized research tools: they’re everyday academic companions. As AIs integrate more deeply into educational environments, we need to consider important questions about learning, assessment, and skill development. Until now, most discussions have relied on surveys and controlled experiments rather than direct evidence of how students naturally integrate AI into their academic work in real settings.

In case you missed it, Carnegie Learning published a great little report, "The State of AI in Education 2025" which is essentially key findings from a national survey they conducted on hundreds of educators. Not too surprisingly, the #1 potential benefit of students using AI was the newest answer choice added to this year’s survey:…

Library and outside resources to enrich your understanding of AI and its place in the classroom and beyond.

The AI Pedagogy Project is a curated collection of resources for educators curious about how AI affects their students and their syllabi

So let’s get one thing out of the way: I think “AI literacy” is a dangerous device of neoliberal education and it deserves to be dismissed out of hand.

This free student guide to artificial intelligence is available for students and institutions to download, distribute, and adapt.

Everything is about to change.

Background The public release of ChatGPT in November 2022 sparked a boom and public interest in generative artificial intelligence (AI) that has led to journals and journal families hastily releasin...

Rapid developments in artificial intelligence (AI) require dynamic adaptation in education to integrate new technologies timely and sustainably. In particular, the rise of generative AI requires leadership to implement it in a meaningful way for teaching and learning. School leaders have a special role to play in driving digital transformation. Based on a sample of German school leaders, this article explores how school leadership approaches and a digital mindset influence the implementation of AI in schools. Our findings provide initial and preliminary evidence that school leaders’ digital mindsets, particularly proactive agility and empathy, understood as perspective taking, influence the implementation of AI in schools. Furthermore, the findings highlight the effectiveness of ambidextrous leadership in driving AI implementation. As a consequence, our study paves the way for future explorations of the evolving landscape of AI in education and highlights the need for adaptive, empathetic, and proactive leadership in the digital age.

The academic questions posed by the introduction of Generative AI (GenAI) into design higher education are many and reflect educators’uneasiness about its ethical use. Like any new radical technolo...

The emergence of generative artificial intelligence (GenAI) requires innovative educational environments to leverage this technology effectively to address concerns like academic integrity, plagiarism, and others. Additionally, higher education needs effective pedagogies to achieve intended learning outcomes. This emphasizes the need to redesign active learning experiences in the GenAI era. Authentic assessment and experiential learning are two possible meaningful alternatives in this context. Accordingly, this article investigates how GenAI can enhance teaching and learning by constructively addressing study situations beyond conventional learning approaches and cultivating high-order skills and knowledge acquisition. This study employs thing ethnography to examine GenAI tools’ integration with authentic assessment and experiential learning and explore implementation alternatives. The results reveal insights into creating human-centered and GenAI-enhanced learning experiences within a constructive alignment. Specific examples are also provided to guide their implementation. Our contributions extend beyond the traditional use of GenAI tools as mere agents-to-write or agents-to-answer questions to become agents-to-support experiential learning for authentic assessment. These findings underscore the transformative role of GenAI tools in enhancing teaching and learning efficacy and effectiveness. The limitations in treating GenAI tools as subjects in thing ethnography are acknowledged, with potential for future implementation evaluation.

This qualitative study explores the levels of technology acceptance of students and teachers in higher education regarding the use of artificial intelligence (AI) in summative assessment. Twelve st...

The rapid integration of Artificial Intelligence (AI) in Higher Education (HE) is transforming personalized learning, administrative automation, and decision-making. However, this progress...

Design educators are finding ways to support students in skillfully using GenAI tools in their practices while encouraging the critical scrutiny of the ethical and social issues around these...

This article provides a critical thematic literature review that explores the intersection of generative artificial intelligence (GenAI) and social-emotional le...

Redesigning common assignments to be AI-aware

Students, faculty, and staff need to understand the fundamentals of AI to able to effectively use AI tools and evaluate the outputs.

Ph.D. in Higher Education Leadership (April 2011) Western Michigan University (Kalamazoo, MI) Dissertation: Knowledge, Attitudes, and Instructional Practices of Community College Math Instructors: The Search for a KAP Gap in Collegiate Math M.S. Mathematics (May 2002) University of Wyoming (Laramie, WY) Thesis: Max-Plus Algebra Properties and Applications M.B.A. (August 2001) University of Wyoming (Laramie, WY) […]