The Disappearing Struggle in the Age of AI: Re-Evaluating Pedagogy of Algorithms and Problem Solving in Tool-Augmented Learning Spaces

Abstract

The problem of the boundary in algorithmic learning that has preoccupied scholarly and practical discussion since the inception of generative AI platforms such as ChatGPT and GitHub Copilot seems to be drifting into the past. In this paper, the study conceptually examines the issue of tool-augmented environments by addressing the choice and degree to which students can bypass the kind of cognitive challenge that in the past was required to achieve the sophistication in recursive logic, complexity analysis or optimization strategies. The introduction to the paper shifts the narrative of AI negatively by conceptualizing a pedagogical re-imagination where AI would not be positioned as an existential threat, but as a co-reasoning partner instead. Divide and Conquer, Sorting, Space and Time Complexity, Dynamic Programming, and Greedy Algorithms case studies are provided, as well as a set of supporting educational interventions that will enable driving critique, reconstruction, and metacognition over time. The notions of recursion, fault injection, prompt engineering, and constraint-based tool design can be viewed as an illustrative example of how productive struggle can be maintained in AI-assisted environments. Focused on such integration of empirical inquisition, the theory of learning, and design-based thinking, the paper proposes a new vision: a vision where automation supplements rather than replaces conceptual mastery, where algorithmic thinking is deeply human, critical, and creative.