Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

TL;DR

This review maps the current landscape of generative AI in computing education using a three-pronged methodology: a systematic literature review (SLR), international surveys of educators and industry developers, and qualitative interviews with educators and GenAI tool builders. It distinguishes two primary use cases: teaching students to use GenAI and leveraging GenAI to support teaching practices, with guardrails and guidance emerging as key factors for positive outcomes. The findings indicate widespread use of general GenAI tools, rapid evolution of competencies toward higher-level thinking, and concerns about equity, cheating, and deep learning. The work highlights policy gaps, the need for PD, and industry–education alignment to ensure effective, ethical, and scalable AI-enabled education in computing.

Abstract

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.

Figures (10)

• Figure 1: The literature review collection and analysis pipeline.
• Figure 2: Heatmaps of where authors publishing are located (Figure \ref{['subfig:author_map']}) and where the student populations they are investigating are located (\ref{['subfig:student_map']})
• Figure 3: The programming languages reported on by the studies.
• Figure 4: Quality of work as evaluated using the paper quality metrics of hellas2018predicting.
• Figure 5: Instructors integrated GenAI into a wide variety of course sizes (ES-13).