ENGR 112: Mechanics of Materials

Pre-lecture Reading, Step-Adaptive AI, Shear–Moment Mastery

Course Description

ENGR 112—Mechanics of Materials is a lower-division, major-required course that examines how deformable solids and structural elements carry load and deform, with emphasis on internal forces (axial, shear, and bending moment), stress–strain behavior, shear–moment diagrams, bending and deflection, combined loading, and the disciplined use of free-body diagrams with correct units and sign conventions. The department typically offers four or five sections per semester, each capped at 30 to 35 students. In Fall 2025, I am teaching two sections. The course is delivered in person as a lecture.

Overview of the Redesign Project

I redesigned ENGR 112 to close foundational gaps that surface later in an upper-level course, CE 160 (Structural Analysis). Across all units, I rebuilt the lecture arc around active learning: short pre-lecture readings to prime concepts; quick Socrative checks for real-time feedback; brief physical demonstrations (e.g., foam-beam deflection under different supports and load cases) to anchor theory; an instructor-worked model-problem segment to make expert reasoning visible; and parallel individual or small-group problem solving that culminates in weekly practice sets.

Within that course-wide redesign, I am also piloting an AI-guided workspace (ChatGPT - study mode) in the shear–moment module—historically a bottleneck—so students practice a five-stage routine (Free-Body Diagrams → reactions → qualitative line-slope-moment scaffold → key values via signed areas → consistency checks) with tiny, targeted hints only when specific mistakes occur, and an equivalent non-AI pathway for students who prefer it. The goals are to strengthen conceptual reasoning and accuracy (fewer missed jumps, wrong slopes, and boundary errors), increase student confidence, and model ethical, privacy-aware use of AI as a learning aid rather than a copy-and-paste shortcut.

This redesign aligns with the Civil Engineering program’s practice-oriented mission and ABET outcomes, and with College and campus priorities for student success, engaging pedagogy, and responsible integration of emerging technologies that prepare career-ready graduates.

Assessment of the Redesign Project

To check learning across all ENGR 112 units, I used quick, low-stakes checks alongside focused unit assessments. First, I added pre-lecture reading assignments tied explicitly to a tentative syllabus schedule, linking every topic to its corresponding textbook section. Understanding was checked with Socrative quick quizzes at the start of class; these gave real-time feedback to students and me, and their item-level patterns flagged concepts to revisit immediately. In class, I work through example problems with students by asking guiding questions and solving the steps together; when ideas aren’t landing, I reinforce concepts with quick physical demos. Each week, students complete a set of five problems to build mastery, submit their work as a simple PDF through Gradescope (where they can view the rubric), and I check deeper understanding later on the midterm exams.

For the shear–moment diagrams module—our historical bottleneck—I ran a simple A/B comparison across my two matched sections (75 minutes, same content/pacing): Week 13 used the traditional graphical method (no AI); Week 14 uses an AI-guided workspace (free accounts, guided mode, side-by-side canvas). To keep the comparison fair, both sections worked the same problem statements on the same day and then paired isomorphic versions across practice modes. Students exported their work (PDF) and submitted it to the LMS. Impact metrics include pre/post concept checks around the unit, process data (time-on-task and coded error types—jump, slope, peak moment, boundary checks), and a one-minute survey on confidence and perceived mental effort. The comparison emphasizes pre-to-post gains, shifts in error patterns, and consistency on a later transfer item, with the AI portion in progress in Week 14.

This table shows three ways I assessed my course redesign.
Syllabus: tentative schedule with pre-readings	Socrative pre-reading quiz (samples)	AI-guided workspace: Traditional S–M notes → Isomorphic problems (no-AI & AI)
Each class lists a topic and the exact textbook section for pre-reading. This set students up for the in-class quiz and helped reduce ‘cold starts’ in discussion.	Quick, low-stakes check on key ideas from the reading. I used the item results in real time to reteach or move on.	Lecture notes (traditional graphical approach - week 13): a slide showing the step-by-step method for shear–moment diagrams. Isometric problems for students (week 14): Problem A (no AI) and Problem B (AI allowed)

Reflection

Successes and challenges

The redesign has made class time more active and focused. Pre-reading tied to the textbook, quick Socrative checks, and short physical demos help students arrive prepared and talk through ideas while we solve examples together. Weekly five-problem sets on Gradescope (with visible rubrics) keep practice consistent and make feedback clearer. In the shear–moment module, the traditional week (Week 13) is complete; the AI-guided week (Week 14) is scheduled next, so the fair comparison is in progress. The main challenges have been pacing a full 75-minute session, standing up materials and rubrics the first time, and handling tech logistics (Socrative timing, device access, and clear guidance on ethical AI use). Comfort with AI also varies; some students need reassurance that the tool supports—not replaces—their own reasoning.

What we gained and next steps

Students are building stronger habits: they set up problems carefully, keep track of units and signs, and can explain what their diagrams mean (how forces move through a structure). With frequent, low-stakes practice, their confidence is growing. I’m also getting earlier signals about where they get stuck and a clear, repeatable way to guide them.

Next, I’ll run the Week 14 lesson that uses an AI-supported workspace, then review quick before/after checks and common mistake patterns to refine the short hints students receive. I’ll add more pairs of similar practice problems, streamline the “quick quiz + short demo” routine, extend this approach to other tough topics, and share materials with colleagues so the updates align with department goals and can be adopted across sections.

Faculty Biography

This table includes my faculty biography.
	Dr. María José Echeverría is a first-generation college graduate from Ecuador, with degrees in Civil Engineering and Law. She holds dual PhDs in Civil Engineering from Pontificia Universidad Católica de Chile and the University of Colorado Boulder, where her work centered on earthquake and structural engineering. In 2024, she joined Sacramento State as an Assistant Professor, where she has felt welcomed and supported, receiving both the Probationary Faculty Development Grant and the Research and Creativity Award. Deeply committed to mentorship and educational equity, María José strives to empower underrepresented students to succeed in engineering. She is also increasingly engaged in exploring the role of artificial intelligence in engineering education, particularly how AI can enhance active learning and broaden student access to high-quality instructional support.

**Program Sponsors**
	This work was supported by: Sacramento State Academic Affairs - Center for Teaching and Learning