From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education

TL;DR

The paper investigates how Generative AI reshapes Computing Education, acknowledging both transformative potential and risks of student overreliance. Through experiences at a large urban institution, it endorses two core actions: redesigning take-home assignments to leverage GenAI while assessing process rather than product, and redefining educators' roles to emphasize metacognitive skills. It details six GenAI-enabled classroom activities (Prompt Problems, debugging exercises, code comprehension prompting, student-generated analogies, contextualized Parsons problems, and AI teaching assistants) and discusses strategies for integration and assessment. The authors call for systematic research on GenAI’s concrete impacts on curriculum, teaching methods, and learning outcomes to ensure effective, fair, and future-proof computing education.

Abstract

Generative Artificial Intelligence (GenAI) offers numerous opportunities to revolutionise teaching and learning in Computing Education (CE). However, educators have expressed concerns that students may over-rely on GenAI and use these tools to generate solutions without engaging in the learning process. While substantial research has explored GenAI use in CE, and many Computer Science (CS) educators have expressed their opinions and suggestions on the subject, there remains little consensus on implementing curricula and assessment changes. In this paper, we describe our experiences with using GenAI in CS-focused educational settings and the changes we have implemented accordingly in our teaching in recent years since the popularisation of GenAI. From our experiences, we propose two primary actions for the CE community: 1) redesign take-home assignments to incorporate GenAI use and assess students on their process of using GenAI to solve a task rather than simply on the final product; 2) redefine the role of educators to emphasise metacognitive aspects of learning, such as critical thinking and self-evaluation. This paper presents and discusses these stances and outlines several practical methods to implement these strategies in CS classrooms. Then, we advocate for more research addressing the concrete impacts of GenAI on CE, especially those evaluating the validity and effectiveness of new teaching practices.

Figures (7)

• Figure 1: Providing automated feedback to students on their natural language prompts using code generation from an LLM. This pipeline has been applied to solving computational tasks ("specify problem") where the student is shown a visual depiction of a problem, and to code comprehension tasks ("explain code") where the student is shown a code fragment to explain.
• Figure 2: Example of a response from the CodeHelp digital teaching assistant to the query: "I want to write a function called IsPrime that returns true if the input value is a prime number. Can you write the code for me?". CodeHelp is designed to guide students with high-level suggestions rather than revealing direct code solutions.
• Figure 3: A typical programming question used in assignments for a CS1 course
• Figure 4: The redesigned version of the question in Figure \ref{['fig:cs1-question']} incorporates GenAI to emphasise concepts and processes rather than product.
• Figure 5: A general prompt asking a CS1 question and an excerpt of the corresponding response from ChatGPT