Unit III Review: Spoken Discourse Deconstruction & Pedagogical Role-Play Design

Description

In this collaborative, two-part review sequence, you and your partner will synthesize the core theoretical and analytical frameworks of Unit III: Spoken Interaction. Working directly with natural conversational transcripts, course readings, and synthetic generative AI outputs, you will audit the conversational mechanics of everyday spoken English versus sanitized textbook dialogue. Through this hands-on inquiry, you will identify speech acts, turn-taking signals, and preference organization to bridge raw linguistic analysis into an inclusive English Language Teaching (ELT) role-play task.

Goal

Collaborate in pairs to analyze spoken discourse mechanics across authentic transcripts and AI-generated dialogue, transforming your analytical findings into an evidence-based, Universal Design for Learning (UDL)-aligned speaking role-play.

You Will Need:

• Course textbook and readings:

• Cook, G. (1989). Discourse, Section 1: Spoken Discourse.

• McCarthy, M. (1991). Discourse Analysis for Language Teachers, Chapter 5: Spoken Language.

• Searle, J. R. (1969). Speech Acts.

• Accompanying Handout: Unit III Analytical Protocol & Role-Play Design Worksheet (attached below).

• Colored highlighters (digital or physical) to tag Speech Acts, Adjacency Pairs, and Dispreferred Turn Markers.

• Access to a Generative AI tool (e.g., ChatGPT, Claude, Microsoft Copilot).

• Universal Design for Learning (UDL) Guidelines reference (Multiple Means of Engagement, Representation, and Action & Expression).

Time:

Two 50-minute class sessions (Session 1: Today; Session 2: Tomorrow).

List of Outcomes:

By the end of this two-day review task, you and your partner will be able to:

1. Differentiate and annotate locutionary acts, illocutionary force, and perlocutionary effects in natural spoken utterances.
2. Identify and evaluate conversation analysis (CA) mechanics, including adjacency pairs, preference organization (preferred vs. dispreferred turns), and Transition Relevance Places (TRPs).
3. Audit and critique GenAI-generated dialogue scripts to pinpoint artificiality, pragmatic drift, hyper-formality, and missing repair sequences.
4. Design and calibrate a communicative ELT role-play activity that incorporates explicit UDL scaffolds for diverse language learners.

Thursday, October 22, 2026: Spoken Interaction Analysis & Critical AI Dialogue Audit

• Phase 1: Pair Formation & Theoretical Calibration (10 minutes)

1. Pair up with a peer. Access your copies of McCarthy (1991, Ch. 5) and Cook (1989, Section 1).
2. Open the accompanying Unit III Handout and complete Part 1: Conceptual Alignment, matching core concepts (Locutionary/Illocutionary/Perlocutionary, Adjacency Pairs, Preference Organization, Transactional vs. Interactional talk) with authentic spoken examples.

UDL Overview (Multiple Means of Representation — Guideline 2.5): Utilize the visual taxonomy chart provided on page 1 of your handout. It pairs theoretical definitions with symbolic icons and syntactic stems to clarify the distinction between what is said (locution) and what is done (illocution).

• Phase 2: Authentic Spoken Coding (15 minutes)

1. Turn to Part 2 of your handout containing Transcript A (an authentic service encounter).
2. In pairs, code the transcript:

• Label each turn’s illocutionary force.

• Identify the primary adjacency pair and determine whether the response is preferred or dispreferred.

• Mark the Transition Relevance Places (TRPs), overlaps (//), and backchannels (mhm, yeah).

• Phase 3: GenAI Generation & Critical Audit (20 minutes)

1. Open your LLM tool (Level 4 AI Practice).
2. Input the following standard prompt:“Generate an authentic 10-to-12 turn spoken dialogue between an English language student and a university administrative coordinator where the student is politely requesting an extension on a major assignment deadline.”
3. Paste the generated dialogue into Part 3 of your handout.
4. Conduct a line-by-line critical audit. Use highlighters to flag:

• Pragmatic Hyper-correctness: Turns that read like written essays rather than spoken dialogue.
• Missing Conversational Grease: Absence of pauses, false starts, fillers (well, you know), or repair sequences.
• Symmetry Flaws: Artificial, equal-length turn-taking that ignores power dynamics and social distance.

AI Aside (Pragmatic Drift & Statistical Predictability): LLMs are trained to maximize grammatical completeness and textual coherence. Because of this, they default to hyper-articulate, sanitized sentences. As language educators, you must train your analytical eye to identify where AI produces grammatically flawless dialogue that is pragmatically lifeless and unnatural for spoken interaction.

• Phase 4: Daily Debrief & Artifact Check (5 minutes)

1. Save your audited transcript. Ensure both partners have recorded notes in Part 3 of the handout.
2. Submit your Day 1 progress check to Google Classroom.