AI Pedagogy @ SCRP Summer 2026

Extending Pedalogical with Voice, Communication Theory, and AI Literacy

Author
Affiliation

Dr. Lucas Cordova

Willamette University

Published

February 6, 2026

Abstract
I will be presenting on the work I am doing this summer to extend Pedalogical with voice, communication theory, and AI literacy.

Who I Am and What I Do

At Willamette University

Cartoon me. I like coffee and dogs.

Dr. Lucas Cordova

  • CS Professor at SCIS
  • Teach courses in CS and Data Science like software development, data structures, algorithms, data engineering, and more
  • Lab focuses on human-centered learning tools and AI-powered learning

Working on Pedalogical?

An AI platform built for learning, not just answers

Pedalogical is an AI-infused platform developed by students in the Cordova Lab at Willamette University. It helps instructors quickly build high-quality assessments and learning experiences without needing to become experts in educational technology or AI tooling.

What it already does

  • Supports multiple assessment types for any discipline, including textual and chat-based assessments designed to strengthen understanding and explanation

  • Grounded in psychological learning theories:

    • Feynman Technique: learn by explaining in your own words
    • Zone of Proximal Development: meet learners where they are
    • Cognitive Load Theory: keep feedback focused and manageable

Feynman Technique Chatbot Example

Pedalogical in Action

This Summer: Three Big Extensions

Summer 2026 roadmap

flowchart LR
    A["Socio-Cultural<br>Communication<br>Theory"] --> D["A Smarter,<br>More Human<br>Pedalogical"]
    B["Oral<br>Assessment"] --> D
    C["Prompt Engineering<br>Training"] --> D

We are extending Pedalogical in three major directions this summer, each designed to make the platform more responsive, more human, and more useful across disciplines.

Direction 1: Communication Theory

Learning is communication, not just correctness

We are expanding our theoretical foundation to include socio-cultural communication theory, which treats learning and explanation as situated communication that depends on:

  • Audience and context
  • Social norms and expectations
  • Purpose and intent

The goal is to make Pedalogical’s feedback more sensitive to how learners express knowledge in real settings, not just whether they have the correct information.

Direction 2: Oral Assessment

Say it out loud

We are integrating oral assessment into Pedalogical. Students will be able to practice explaining and expressing their understanding out loud and receive feedback that supports growth in:

  • Conceptual understanding
  • Communication skills
  • Confidence

Beyond the classroom

The pedagogical intent supports oral exams, discussion-based learning, and reflective explanation, but it also connects directly to real-world scenarios:

  • Interview preparation
  • Presentation rehearsal
  • Professional communication practice

Direction 3: Prompt Engineering Training

Teaching students to use AI well

We are building a prompt engineering training module to help learners use generative AI effectively as a learning tool. The focus is on moving beyond one-shot prompting toward iterative workflows:

  • Planning and decomposition
  • Adding constraints and context
  • Verification and revision

The goal is to equip learners with durable AI literacy skills that transfer across disciplines and tasks.

Why This Matters

The big picture

flowchart TD
    A["Students learn<br>to explain"] --> B["Students learn<br>to communicate"]
    B --> C["Students learn<br>to use AI<br>effectively"]
    C --> D["Students become<br>better thinkers"]
    D --> A

Pedalogical is not just a learning tool. It is a platform for building the skills that matter most: articulation, reflection, communication, and critical use of AI. This summer’s work makes it more responsive to real human learning.

Thank You!

Dr. Lucas Cordova

Ford Hall, Room 210