Knowledge Markers: An AI-Agnostic Concept for the Design of Programming Courses

TL;DR

This paper introduces knowledge markers, a simple, AI-agnostic framework for explicitly labeling learning units in programming courses to improve course design and student understanding.

Contribution

It proposes a lightweight, operational approach to label course content by knowledge emphasis, aiding instructors in course structuring without relying on AI-specific methods.

Findings

Markers can be embedded in various teaching artifacts.

Analysis and redesign of a programming course demonstrated the approach.

The approach is descriptive; empirical learning gains are future work.

Abstract

Generative AI enables students to produce plausible code quickly. Producing working code is therefore no longer a reliable indicator of understanding. This is particularly problematic in non-computer-science programmes, where time constraints make it hard to balance conceptual foundations with sufficient application practice. Empirical studies of AI tutors, educational chatbots, and code-assistance systems report useful but often case-specific findings, while learning theory remains too abstract to directly guide course design. As a result, instructors lack a simple, reusable way to make learning intent explicit and translate it into concrete teaching structures and student learning behaviour. This paper contributes knowledge markers as a lightweight, AI-agnostic, course-level operationalisation for course design. The markers label learning units by their primary emphasis: (A) Application knowledge (implementation), (S) Structure knowledge (concepts and mental models), or (P) Procedure knowledge (systematic methods, decision making, and verification). We show how the labels can be embedded at fine granularity in open teaching artifacts (interactive website, PDF script, and notebooks), paired with communication elements and optional AI-usage guidance. We demonstrate the approach by analysing, redesigning, and descriptively evaluating an introductory programming course using marker distributions derived from the table of contents. The paper is design- and artifact-oriented and does not claim measured learning gains; empirical evaluation is future work.