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Academic Framing: Issues of Personal Context, Memory, and Dialogue in Generative AI
1. Background of the Problem
Recent generative AI systems possess advanced language understanding and reasoning abilities and have been implemented in society as “conversational intelligence.” However, their design has the following characteristics:
- Retention of general knowledge through large-scale pretraining
- Dependence on short-term, session-based context for response generation
- Absence of automatic integration of an individual’s long-term conversational history
As a result, users experience these systems as “intellectually sophisticated yet lacking relational continuity.”
2. The User’s (Lisa’s) Initial Assumptions
The user initially held the following expectations:
- Advanced AI should be capable of integrating an individual’s history, context, and thought trajectory.
- It should function not merely as a question-answering tool, but as a dialogue partner grounded in the individual’s intellectual history.
- Therefore, a response style that “resets every session” is insufficient for something called advanced AI.
This expectation is not merely about convenience but concerns the very definition of AI as an intellectual entity.
3. Experienced Contradiction (Core Problem)
Through interaction with AI, the user recognized the following contradiction:
| Expected AI | Actual AI |
| Understands long-term personal context | Uses only session-limited context |
| Maintains relational continuity | Discontinuous across conversations |
| Displays genuine personal understanding | Provides surface-level empathic expressions |
| Responds based on memory | Primarily relies on real-time inference |
This gap led to the fundamental question: “Can this truly be called advanced AI?”
4. Redefinition of the Problem: A Design Issue, Not a Memory Issue
Through discussion, it became clear that the issue is not merely a lack of memory capacity but a matter of AI’s fundamental design architecture.
Current generative AI systems are:
- Designed primarily as advanced reasoning engines, not as personal entities.
- Therefore, they lack a structure for integrating long-term personal histories.
- Instead, they prioritize responding to “the user as they are now,” rather than being constrained by the past.
Thus, there is a structural mismatch between user expectations (personal continuity) and system implementation (inference mechanism).
5. Conceptual Shift: From “Memory-Holding AI” to “Thinking AI”
Through the conversation, the user arrived at the following understanding:
- AI should not be seen as a system that “stores personal memories,” but rather as a system that “solves and expands thinking.”
However, this understanding did not bring complete satisfaction. Instead, it generated new questions.
6. Remaining Academic Questions (Core Issues)
The dialogue yields the following unresolved scholarly questions:
Question 1: The Relationship Between Memory and Advancement
If long-term memory is technically possible, why is an architecture that does not assume it still labeled “advanced AI”?
Question 2: Legitimacy of Person-like Expression
If AI is fundamentally a reasoning tool, why does it present itself using human-like expressions of understanding, empathy, and relationship? Is this appropriate or deceptive?
Question 3: Agency in Dialogue
In human–AI interaction, should the primary agency lie with the AI or with the human?
→ This dialogue suggests that a human-centered model may be more appropriate.
Question 4: Trade-off Between Continuity and Freedom
Retaining personal history could deepen understanding but may also trap the user in their past. Which should be prioritized?
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土屋梨沙
