2026 ASEE Annual Conference & Exposition

This Work in Progress Paper examines a redesigned first year engineering course that integrates analysis, design, and technical writing, and pilots a rubric based approach—supported by an inter-rater reliability plan—to assess how students use data and models to justify design decisions in a one-page technical memo.

Motivation: A persistent challenge in first year engineering education is helping students treat mathematical analysis, experimentation, and design as an integrated problem-solving process rather than as separate, sequential tasks. Many students can compute or prototype but struggle to explain why quantitative results matter for decisions or how evidence informs iteration—core habits of engineering reasoning. Responsible use of AI assisted writing tools further raises the stakes for teaching students to communicate decisions with clarity, evidence, and professional judgment.

Background: We frame engineering reasoning as the coordinated use of models, data, and constraints to justify design choices. The course design draws on model-based reasoning, writing to learn, and design thinking principles: students (i) practice formal problem solving on well-defined tasks, (ii) apply and reflect during iterative design, and (iii) synthesize their evidence-based decisions in a concise technical memo addressed to a professional audience. We operationalize reasoning in writing through a rubric inspired by the AAC&U VALUE Written Communication rubric, adapted with engineering specific criteria (integration of mathematical modeling with design reasoning; articulation of the engineering design process; professional clarity and audience aware communication; reflection/justification of results). We additionally include an explicit criterion for how well the engineering design process is integrated within the memo narrative.

Methods: In this engineering fundamentals, student teams complete a five week “Projectile Launcher Challenge,” which entails designing, building, testing, and refining a launcher to meet a target distance. The engineering design sequence embedded in the project is comprised of learning and applying a structured problem-solving framework; engaging in iterative design with guided reflections; and authoring a one-page team technical memo that links models, data, and design choices. We will analyze memos from two offerings: a 2024 baseline (limited scaffolding; no explicit AI guidance) and a 2025 redesign (scaffolded reflections; explicit AI guidance). Each memo is scored on a four-level scale (Beginning, Developing, Proficient, Advanced) for each rubric criterion. Two course instructors will train a set of memos from both offerings to come to agreement on the scales of the memos. Instructors will code the sample memos using the rubric and ensure adequate inter-rater reliability has been reached. Disagreements in ratings will be discussed until an agreement is reached. Further, we will quantitatively compare rubric distributions across offerings using Mann–Whitney tests with effect sizes (Cliff’s δ). Qualitatively, we will sample memos to illustrate high quality reasoning that aligns with the structured engineering design process introduced in the course.

Implications: We anticipate these research findings will support providing FY students with more structured support around problem solving, engineering design, and integration of mathematical concepts.