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The Role of AI in Assessment: Revolutionizing Education
by Xinhui Xiong, Mark D. Shermis, and Jiawei Xiong

Artificial intelligence is reshaping the way we measure learning — and this book tackles that shift head-on. Written by Xinhui Xiong, Mark D. Shermis, and Jiawei Xiong, it offers a clear-eyed look at how AI is changing the landscape of educational assessment, from automated scoring to adaptive testing and beyond.

Rather than treating AI as a buzzword, the authors dig into the real opportunities it creates and the real limitations educators need to watch out for. It’s a timely read for anyone trying to build assessment tools that are not just powerful, but fair, transparent, and grounded in sound practice — meeting the growing call for AI use in education that’s both effective and responsible.

Whether you’re a researcher, educator, or edtech developer, this book gives you a practical, no-fluff framework for thinking through what AI-driven assessment should look like going forward.

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FAQs :-

1. Who are the authors, and what makes them qualified to write this?

The book is edited by Xinhui Xiong, Mark D. Shermis, and Jiawei Xiong. Dr. Xinhui Xiong is Chief Psychometrician at Examroom AI, with a PhD in Psychometrics from Fordham University, an MLA in Management from Harvard, and a Master’s in Computer Science, and her work focuses on scalable assessment design including validation, equating, fairness analysis, and test security. Dr. Mark D. Shermis was the principal investigator and academic adviser for the Automated Student Assessment Prize (ASAP) and is a well-known name in automated essay evaluation research. This isn’t a book written by outsiders speculating on AI — it’s written by people who’ve actually built and validated these systems.

2. What topics does the book actually cover?

It goes well beyond a general “AI is changing education” overview. The book explores practical applications such as automatic item generation, automated essay scoring, game-based assessment, and test security, and introduces foundational AI concepts like reinforcement learning, neural networks, deep learning, and large language models in a way that’s accessible even if you’re not a data scientist. There are also dedicated chapters on ethics, equity, and personalized learning, so it balances the technical side with the human side.

3. Who is this book actually written for?

It’s aimed at educators, researchers, and policymakers — so it works whether you’re a classroom teacher trying to understand what’s coming, a grad student researching AI in measurement, or someone in an administrative or policy role trying to make informed decisions about adopting these tools.

4. When is it being published, and is it available now?

The book is copyright 2027 under Routledge, which means it’s a newer, forward-looking release rather than something already outdated by the pace of AI development — a real plus in a field that moves this fast.

5. Does it just praise AI, or does it also cover the risks and limitations?

It does both, deliberately. The book analyzes both the opportunities and the constraints of AI-driven assessment, responding to the growing demand for approaches that use AI rigorously and responsibly — so it’s not a hype piece. It’s positioned as a balanced, practical guide rather than a sales pitch for AI adoption.

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