Seyma Yaman Kayadibi

Seyma Yaman Kayadibi

This study reinterprets a Monte Carlo simulation of students' perceived success with generative AI (GenAI) through a Kantian-axiomatic lens. Building on prior work, theme-level survey statistics Ease of Use and Learnability, System Efficiency and Learning Burden, and Perceived Complexity and Integration from a representative dataset are used to generate 10,000 synthetic scores per theme on the [1,5] Likert scale. The simulated outputs are evaluated against the axioms of dense linear order without endpoints (DLO): irreflexivity, transitivity, total comparability (connectedness), no endpoints (no greatest and no least; A4-A5), and density (A6). At the data level, the basic ordering axioms (A1-A3) are satisfied, whereas no-endpoints (A4-A5) and density (A6) fail as expected. Likert clipping introduces minimum and maximum observed values, and a finite, discretized sample need not contain a value strictly between any two distinct scores. These patterns are read not as methodological defects but as markers of an epistemological boundary. Following Kant and Friedman, the findings suggest that what simulations capture finite, quantized observations cannot instantiate the ideal properties of an unbounded, dense continuum. Such properties belong to constructive intuition rather than to finite sampling alone. A complementary visualization contrasts the empirical histogram with a sine-curve proxy to clarify this divide. The contribution is interpretive rather than data-expansive: it reframes an existing simulation as a probe of the synthetic a priori structure underlying students' perceptions, showing how formal order-theoretic coherence coexists with principled failures of endpoint-freeness and density in finite empirical models.

Seyma Yaman Kayadibi

Artificial intelligence is observed to age not through chronological time but through structural asymmetries in memory performance. In large language models, semantic cues such as the name of the day often remain stable across sessions, while episodic details like the sequential progression of experiment numbers tend to collapse when conversational context is reset. To capture this phenomenon, the Artificial Age Score (AAS) is introduced as a log-scaled, entropy-informed metric of memory aging derived from observable recall behavior. The score is formally proven to be well-defined, bounded, and monotonic under mild and model-agnostic assumptions, making it applicable across various tasks and domains. In its Redundancy-as-Masking formulation, the score interprets redundancy as overlapping information that reduces the penalized mass. However, in the present study, redundancy is not explicitly estimated; all reported values assume a redundancy-neutral setting (R = 0), yielding conservative upper bounds. The AAS framework was tested over a 25-day bilingual study involving ChatGPT-5, structured into stateless and persistent interaction phases. During persistent sessions, the model consistently recalled both semantic and episodic details, driving the AAS toward its theoretical minimum, indicative of structural youth. In contrast, when sessions were reset, the model preserved semantic consistency but failed to maintain episodic continuity, causing a sharp increase in the AAS and signaling structural memory aging. These findings support the utility of AAS as a theoretically grounded, task-independent diagnostic tool for evaluating memory degradation in artificial systems. The study builds on foundational concepts from von Neumann's work on automata, Shannon's theories of information and redundancy, and Turing's behavioral approach to intelligence.

Seyma Yaman Kayadibi

The exponential development of generative artificial intelligence (GenAI) technologies like ChatGPT has raised increasing curiosity about their use in higher education, specifically with respect to how students view them, make use of them, and the implications for learning outcomes. This paper employs a hybrid methodological approach involving a systematic literature review and simulation-based modeling to explore student perceptions of GenAI use in the context of higher education. A total of nineteen empirical articles from 2023 through 2025 were selected from the PRISMA-based search targeting the Scopus database. Synthesis of emerging patterns from the literature was achieved by thematic categorization. Six of these had enough quantitative information, i.e., item-level means and standard deviations, to permit probabilistic modeling. One dataset, from the resulting subset, was itself selected as a representative case with which to illustrate inverse-variance weighting by Monte Carlo simulation, by virtue of its well-designed Likert scale format and thematic alignment with the use of computing systems by the researcher. The simulation provided a composite "Success Score" forecasting the strength of the relationship between student perceptions and learning achievements. Findings reveal that attitude factors concerned with usability and real-world usefulness are significantly better predictors of positive learning achievement than affective or trust-based factors. Such an interdisciplinary perspective provides a unique means of linking thematic results with predictive modelling, resonating with longstanding controversies about the proper use of GenAI tools within the university.