Knowing what AI literacy is, but not yet how to teach it: A rapid review of teachers’ professional development needs and effective principles in higher education
This rapid review of 16 studies on higher education teachers' AI literacy professional development identifies five key needs and ten interventions but concludes that while evidence on teachers' needs is emerging rapidly, robust research on effective professional development approaches remains limited.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine a classroom where the tools for learning are changing faster than the textbooks can be updated. For decades, teachers have guided students through the process of writing essays, solving problems, and thinking critically. Today, a new kind of technology has arrived that can write those essays, solve those problems, and generate ideas in seconds. This technology, known as generative artificial intelligence, is not just a new gadget; it is reshaping how knowledge is created and shared. When a student can ask a computer to draft an entire assignment, the role of the teacher shifts. They are no longer just the source of answers but the guide who helps students navigate when to use these tools, how to check if the information is true, and how to use them without losing their own voice. To do this, teachers need a new set of skills, often called AI literacy. This is not just about knowing how to press buttons; it is about understanding how to weave these powerful tools into lessons in a way that helps students learn rather than submit work without understanding, and how to teach them to think critically about what the machine produces.
A team of researchers set out to understand how universities are preparing their teachers for this shift. They conducted a rapid review, a method designed to gather and analyze the latest available evidence quickly, to see what teachers need to learn and what training methods actually work. They searched through academic studies published between 2016 and early 2026, narrowing their focus down to sixteen studies that specifically looked at higher education teachers and their training. The researchers were looking for two main things: what teachers say they need to learn, and what kinds of training programs have been tested to see if they help.
The picture that emerged from these sixteen studies reveals a clear gap between what teachers need and what they are currently getting. When asked about their needs, teachers across different countries and subjects pointed to five main areas. First, they want practical training that is directly connected to their daily work. They do not want abstract lectures on how computers think; they want to know how to use these tools to design a specific lesson or grade a specific type of assignment. Second, they need help with the ethical side of things. Teachers are worried about bias in the tools, questions of who owns the work, and how to teach students to use the technology responsibly. Third, they expressed a strong desire for ongoing support. They do not want a one-time workshop; they want a continuous process where they can learn, share ideas with colleagues, and keep up with new updates. Fourth, they need their institutions to provide clear rules and policies. Teachers are unsure about what is allowed and what is not, and they need their universities to set clear guidelines on academic integrity and data privacy. Finally, they need training that fits their specific subject. A teacher in engineering has different needs than a teacher in literature, and a one-size-fits-all approach does not work.
Despite these clear needs, the researchers found that the training programs currently available often fall short. They analyzed ten specific training interventions described in the studies. Most of these programs focused on the basics: teaching teachers how the technology works, how to use it for writing or creating images, and how to apply it to their teaching. They also covered ethics. While these programs were generally well-received and showed positive results in the studies that tested them, the researchers noted a significant problem. The training often ignored the broader needs that teachers had identified. Very few programs addressed the need for long-term collaboration, institutional policy support, or subject-specific guidance. The training was often a single event, like a workshop or a short course, rather than a sustained journey.
Furthermore, the researchers found that it is difficult to say exactly which parts of these training programs make them successful. Many of the studies describing the programs were vague about the details. They might say a workshop happened, but they did not explain how long it lasted, what the teachers actually did during the session, or how the teachers were supported afterward. This lack of detail makes it hard to know what to copy or improve. The researchers concluded that while we now have a good understanding of what teachers need to learn, we do not yet have strong evidence on the best ways to teach it. The field is moving so fast that the research is struggling to keep up. The studies suggest that effective training will need to be more than just a technical course; it must be a continuous, collaborative effort supported by clear rules and tailored to the specific realities of each teacher's classroom. Until more rigorous studies are done, universities are left trying to build these training programs with a clear map of the destination but only a rough sketch of the path to get there.
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