"CS 1.5": An Experience Report on Integrating CS1 and Discrete Structures for the AI Era

• URL: http://arxiv.org/abs/2604.16365v1
• Date: Sat, 21 Mar 2026 21:15:59 GMT
• Title: "CS 1.5": An Experience Report on Integrating CS1 and Discrete Structures for the AI Era
• Abstract summary: generative AI has fundamentally altered the landscape of introductory computer science education.<n>In response, we designed and implemented an experimental course integration at Northeastern University Vancouver.<n>This report details our pedagogical interventions, including the restructuring of the timetable to support a 4-hour studio format.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of generative AI has fundamentally altered the landscape of introductory computer science education. Traditional methods that prioritize syntax memorization and writing code from scratch are challenged by tools that can generate such code instantly. In response, we designed and implemented an experimental course integration at Northeastern University Vancouver, merging "Intensive Foundations of Computer Science" (CS1) and "Discrete Structures" into a single, cohesive studio experience. Dubbed "CS 1.5"--a playful nod to its position between CS1 and CS2--this course operates on two core principles: embracing AI as a collaborator rather than an adversary, and prioritizing deep theoretical foundations alongside practical implementation. This report details our pedagogical interventions, including the restructuring of the timetable to support a 4-hour studio format, the introduction of "sharing circles" to foster human connection, and the strategic shift to "code comprehension" over code generation. We discuss specific integrated projects--spanning set theory, recursion, and probability--that bridge the gap between mathematical proofs and software implementation. Finally, we reflect on the changing role of the instructor--from a repository of knowledge to a human mentor--and offer practical recommendations for scaling this high-touch, integrated model.

Related papers

• LearnAI: Just-in-Time AI Co-Creation Across Disciplines at a University [2.980329026942995]
  This report presents the LearnAI Framework, a two-layer model for just-in-time AI co-creation piloted at a comprehensive teaching university.<n>The Wide-Exposure Layer embeds short presentations in existing courses to build AI awareness at scale, reaching students and faculty across 18 courses in five disciplines.<n>The Customized Co-Creation Layer provides opt-in, one-on-one sessions where clients work with trained undergraduate tutors through a 5-Stage Pedagogical Script.<n>Interviews with five clients and two tutors suggest a recurring change in how clients described AI use, shifting from treating AI as a passive answer machine to engaging
  arXiv  Detail & Related papers  (2026-08-19T17:44:54Z)
• What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code [72.9921566968371]
  We revisit the claim that code improves reasoning through controlled pretraining experiments on a 10T-token corpus with fine-grained domain separation.<n>Code substantially improves programming ability but does not act as a general reasoning enhancer.<n>Cross-domain structured reasoning traces, such as code-text and math-text mixtures, are better explained.
  arXiv  Detail & Related papers  (2026-05-19T12:37:01Z)
• From Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors [2.1212179660694104]
  UNIV 182 is a prerequisite-free course that teaches undergraduates across majors to understand, use, evaluate, and build AI systems.<n>The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions.
  arXiv  Detail & Related papers  (2026-03-19T23:18:43Z)
• Beyond the Code: A Multi-Modal Assessment Strategy for Fostering Professional Competencies via Introductory Programming Projects [1.8810916321241071]
  This paper reports on a "Fundamentals of Programming" course that used a Project-Based Learning framework to develop a 2D "Maze Runner"-style game.<n>Our findings suggest that this multi-dimensional approach improves student retention of programming fundamentals.
  arXiv  Detail & Related papers  (2026-03-19T10:42:34Z)
• IQuest-Coder-V1 Technical Report [56.045533451719905]
  We introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs)<n>IQuest-Coder-V1 achieves state-of-the-art performance among competitive models across critical dimensions of code intelligence.
  arXiv  Detail & Related papers  (2026-03-17T16:15:31Z)
• Statistical Learning Theory in Lean 4: Empirical Processes from Scratch [57.00315741159824]
  We present the first comprehensive Lean 4 formalization of statistical learning theory (SLT) grounded in empirical process theory.<n>Our end-to-end formal infrastructure implement the missing contents in latest Lean 4 Mathlib library.<n>This work establishes a reusable formal foundation and opens the door for future developments in machine learning theory.
  arXiv  Detail & Related papers  (2026-02-02T16:24:53Z)
• Revisiting Software Engineering Education in the Era of Large Language Models: A Curriculum Adaptation and Academic Integrity Framework [0.0]
  This paper proposes a theoretical framework for analyzing how generative AI alters core software engineering competencies.<n> Attention is given to computer engineering programs in Turkey, where centralized regulation, large class sizes, and exam-oriented assessment practices amplify these challenges.
  arXiv  Detail & Related papers  (2026-01-06T22:41:47Z)
• "Can you feel the vibes?": An exploration of novice programmer engagement with vibe coding [42.82674998306379]
  "vibe coding" refers to creating software via natural language prompts rather than direct code authorship.<n>This paper reports on a one-day educational hackathon investigating how novice programmers and mixed-experience teams engage with vibe coding.
  arXiv  Detail & Related papers  (2025-12-02T13:32:23Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• A Survey of Vibe Coding with Large Language Models [93.88284590533242]
  "Vibe Coding" is a development methodology where developers validate AI-generated implementations through outcome observation.<n>Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored.<n>This survey provides the first comprehensive and systematic review of Vibe Coding with large language models.
  arXiv  Detail & Related papers  (2025-10-14T11:26:56Z)
• Enhancing Educational Efficiency: Generative AI Chatbots and DevOps in Education 4.0 [0.0]
  This research paper will bring forth the innovative pedagogical approach in computer science education. It uses a combination of methodologies borrowed from Artificial Intelligence (AI) and DevOps to enhance the learning experience in Content Management Systems (CMS) Development.
  arXiv  Detail & Related papers  (2024-04-18T18:45:39Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.