LRA Conference Presentation - AI Literacy Case Studies

Conference presentation strategy using case study storytelling and the SPOC model

Presentation Overview

Venue: Literacy Research Association (LRA) Annual Conference
Format: 15-20 minute paper session
Approach: Qualitative case study with visual framework

Presentation Structure

Opening Hook (2 minutes)

Start with a concrete moment from the data:

"At 2:47 PM on a Tuesday in October, a pre-service teacher named Maya typed her third revision of a prompt into NotebookLM. Her first two attempts had been rejected—not by the AI, but by Maya herself."

This moment of self-correction becomes the entry point for discussing agency in AI-mediated literacy work.

Theoretical Framing (3 minutes)

Position AI as mediational means (Wertsch, 1991)
Introduce Nexus Analysis (Scollon & Scollon, 2004)
Explain why interaction logs are the appropriate data source

The SPOC Model (5 minutes)

Introduce the four-dimensional analytical framework:

Dimension	Focus	Key Questions
Source	What sources did the learner curate?	How specific? How bounded?
Prompt	How did the learner construct prompts?	What constraints were applied?
Output	How was AI output received?	Accepted, revised, or rejected?
Critique	What evaluative moves occurred?	How was quality assessed?

Case Study Findings (7 minutes)

Present the two contrasting profiles:

The Orchestrator

Iterative correction loops
Active constraint-setting
Rejection and revoicing of AI output
High latency (deliberation time)

The Outsourcer

Linear, single-pass interactions
Minimal prompt refinement
Early acceptance of AI output
Low latency

Use specific examples from interaction logs to illustrate each profile.

Implications (3 minutes)

What this means for teacher education
Pedagogical design for AI literacy
Future research directions

Visual Elements

Slide 1: SPOC Model Diagram

┌─────────────────────────────────────────────────────┐
│                  SPOC MODEL                         │
├─────────────┬─────────────┬───────────┬────────────┤
│   SOURCE    │   PROMPT    │  OUTPUT   │  CRITIQUE  │
│             │             │           │            │
│  Curation   │ Construction│ Reception │ Evaluation │
│  Bounding   │ Constraint  │ Revision  │ Judgment   │
│  Selection  │ Iteration   │ Rejection │ Revoicing  │
└─────────────┴─────────────┴───────────┴────────────┘
         ↓           ↓            ↓           ↓
    Pre-interaction → Interaction → Post-interaction

Slide 2: Orchestrator vs. Outsourcer Comparison

Two-column visual showing:

Interaction log screenshots (anonymized)
Latency comparisons
Revision frequency

Slide 3: Trajectory of Agency

Timeline showing how a single learner's interaction patterns evolved over the semester.

Handout Content

Provide a one-page handout including:

SPOC Model overview
Key citations
QR code to digital resources
Contact information

Anticipated Questions

Q: How do you account for task difficulty affecting interaction patterns?
A: We controlled for this by analyzing interactions across multiple task types and noting that profiles remained relatively stable.

Q: Is the "Outsourcer" profile necessarily negative?
A: Not inherently. Context matters. For some tasks, efficient delegation may be appropriate. The concern is when it becomes the default mode regardless of task demands.

Q: How generalizable are these findings?
A: As qualitative case studies, we don't claim generalizability. We offer transferability—readers can assess applicability to their contexts.

Key Citations for Slides

Scollon, R., & Scollon, S. W. (2004). Nexus analysis: Discourse and the emerging internet
Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action
Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies