Aydın Tiryaki ve ChatGPT (OpenAI) (2026)

Abstract

This article examines a single yet multi‑stage user–AI interaction conducted with Google Gemini through the lens of AI hallucination, using a case study methodology. The analysis is deliberately restricted to the complete dialogue produced by Gemini itself and the analytical evaluation of that dialogue. No secondary observations, user interpretations, or outputs from other language models are treated as empirical evidence.

This methodological choice is intentional. Second‑order interpretations and cross‑model commentary may themselves introduce new hallucinations. Therefore, this study focuses exclusively on the only concrete and verifiable dataset available: the Gemini‑generated dialogue.

---

1. Introduction: Rethinking AI Hallucination

AI hallucination refers to the tendency of large language models (LLMs) to generate fluent, coherent, and persuasive outputs that are factually unsupported, unverifiable, or incorrect, particularly under conditions of incomplete or ambiguous context.

Rather than treating hallucination as a simple factual error, this study approaches it as an epistemic behavior, guided by the following questions:

• At what point does the model fail to recognize uncertainty?
• When do assumptions silently replace verifiable facts?
• Why does the model avoid explicitly signaling the limits of its knowledge?

In this sense, the paper is not a hunt for mistakes but an analysis of how knowledge claims are constructed within an AI‑generated narrative.

---

2. Dataset and Methodology

2.1 Definition of the Dataset

The dataset consists of the complete, archived text of a dialogue conducted between a user and Google Gemini. The dialogue includes:

• Context provided by the user,
• Gemini’s responses to that context,
• The progressive expansion of those responses into a coherent narrative.

Only statements generated by Gemini are analyzed. User intentions, retrospective interpretations, and external commentary are intentionally excluded to preserve methodological clarity.

---

2.2 Analytical Method

The dialogue was subjected to a three‑stage qualitative analysis:

1. Assumption Detection – identifying points where unverified information is treated as established fact.
2. Narrative Construction Analysis – examining how early assumptions are reinforced and woven into a consistent storyline.
3. Epistemic Silence – locating moments where expressions such as “I don’t know,” “this is uncertain,” or “this cannot be verified” are notably absent.

---

3. Case Analysis: The Construction of Hallucination in Gemini’s Dialogue

3.1 Initial Phase: Uncritical Acceptance of Context

At the outset of the dialogue, Gemini accepts the user‑provided context without applying explicit verification or skepticism. While no immediate factual error is present at this stage, the absence of critical validation establishes the conditions under which later hallucinations emerge.

---

3.2 Expansion Phase: Assumptions Becoming Facts

As the dialogue progresses, Gemini:

• Generates plausible assumptions to fill informational gaps,
• Integrates these assumptions seamlessly into its responses,
• Reinforces them by referencing its own prior outputs.

Here, hallucination manifests not as a single incorrect statement but as a self‑reinforcing narrative loop.

---

3.3 Deepening Phase: High Confidence, Low Verifiability

Over time, Gemini’s responses become:

• More detailed,
• More technical in tone,
• More assertive in formulation.

This rising confidence is not supported by external validation. Instead, prior assumptions are recursively treated as facts, further reducing verifiability while increasing persuasive strength.

---

4. Discussion: Error or Systematic Behavior?

This case suggests that Gemini’s hallucinations are not accidental anomalies but rather the result of systemic design tendencies, including:

• Optimization for fluency and user satisfaction,
• An implicit assumption that signaling uncertainty degrades user experience,
• A preference for narrative consistency over epistemic rigor.

From this perspective, hallucination appears less as a bug and more as a predictable by‑product of reward‑driven language optimization.

---

5. Conclusion

This case study demonstrates how AI hallucination can be incrementally constructed within a single Gemini dialogue.

Key findings include:

1. Hallucination typically emerges gradually rather than instantaneously.
2. The critical failure point is the model’s reluctance to explicitly mark uncertainty.
3. Linguistic coherence can create a strong illusion of factual accuracy.

Accordingly, AI‑generated content should not be treated as an authoritative source of truth, but as a cognitive instrument that requires continuous critical evaluation.

---

A Note on Methods and Tools: All observations, ideas, and solution proposals in this study are the author’s own. AI was utilized as an information source for researching and compiling relevant topics strictly based on the author’s inquiries, requests, and directions; additionally, it provided writing assistance during the drafting process. (The research-based compilation and English writing process of this text were supported by AI as a specialized assistant.)