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.

Learn the structure of UDL 3.0—Principles, Guidelines, and Practices—and how they guide inclusive, equitable teaching.

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

New features integrated into Canvas include a grading assistant, a discussion-post summarizer, and even a way to pair assignments with generative AI tools.

Streamline your Canvas API development with Atomic Canvas API Docs MCP—an AI-powered assistant that delivers instant endpoint access, smart code generation, and guided workflows inside Claude Desktop.

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

Release notes outline upcoming customer-impacting changes that are expected to be displayed in production environments. Please note that features considered for future development are not included in the notes. Canvas Studio is a communication tool that allows instructors and students to actively co...

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Neuroscience fascinates, but behavior guides instruction. Teachers shouldn't need brain scans to know when learning is really happening.

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

Explore services, tools, and guides to build seamless integrations in the Instructure ecosystem.

Mike Perkins and Jasper Roe share a practical framework for ethical AI integration in assessment on episode 569 of the Teaching in Higher Ed podcast

It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in K-12 and higher education across a range of disciplines. The AIAS has been adopted by schools and universities worldwide, and will […]

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:…

Reflecting on common misconceptions about educator development can help leaders create meaningful change.

Cognitive load theory (CLT) has driven numerous empirical studies for over 30 years and is a major theme in many of the most cited articles published between 1988 and 2023. However, CLT articles have not been compared to other educational psychology research in terms of the research designs used and the extent to which recommendations for practice are justified. As Brady and colleagues found, a large percentage of the educational psychology articles reviewed were not experimental and yet frequently made specific recommendations from observational/correlational data. Therefore, in this review, CLT articles were examined with regard to the types of research methodology employed and whether recommendations for practice were justified. Across several educational psychology journals in 2020 and 2023, 16 articles were determined to directly test CLT. In contrast to other articles, which employed mostly observational methods, all but two of the CLT articles employed experimental or intervention designs. For the two CLT articles that were observational, recommendations for practice were not made. Reasons for the importance of experimental work are discussed.

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