Sophotechnic Mediation Scale

• Sophotechnic Mediation Scale is a validated psychometric instrument measuring internalized cognitive mediation from generative AI engagement.
• It employs a unidimensional structure derived from Cognitive Mediation Networks Theory, using 13 expert-selected items across four domains.
• Its robust reliability, demonstrated by a high Cronbach’s α (~0.94) and excellent factor structure, confirms its empirical significance.

Sophotechnic Mediation Scale (SMS) is a psychometric instrument designed to measure Sophotechnic Mediation, an emergent mode of thinking and acting arising from sustained engagement with generative artificial intelligence (GenAI) systems. Grounded in the Cognitive Mediation Networks Theory (CMNT), SMS captures the internalization of operational invariants attributable to large-language-model–based GenAI platforms through a single, unidimensional latent variable. The scale has been validated in a multi-year (2023–2025), multi-cohort sample of 3,932 adult workers in the Metropolitan Region of Pernambuco, Brazil, demonstrating excellent reliability, robust factor structure, temporal coherence, and explanatory power for GenAI-related cognitive mastery (Souza, 21 Dec 2025).

1. Theoretical Foundation

CMNT frames human cognition as a distributed system, with external mechanisms (e.g., digital technologies) serving as auxiliary information processors that become internalized through repeated interaction. Sophotechnic Mediation is defined as the internalized orchestration of GenAI, encompassing strategic engagement, critical awareness of system limitations and biases, and active participation in new sociotechnical practices. Unlike transient efficiency gains, Sophotechnic Mediation represents a stable, acquired cognitive mechanism distinct from prior digital mediation constructs (such as Hypercultural mediation).

GenAI systems (including ChatGPT, Copilot, Google Gemini) are characterized by natural-language interfaces, advanced generative/inferential capabilities, and rapid societal integration. SMS operationalizes this construct, mapping the transition from externalized interaction to internalized mediation at scale.

2. Instrument Construction and Domains

The SMS was developed using an expert-driven process rooted in CMNT, with initial items spanning four domains: breadth of GenAI utilization, orchestration competence, ethical/critical awareness, and sociotechnical engagement. After pilot testing and removal of items with poor psychometric properties (low item-total correlation, extreme skewness $|skewness|>2$), 13 items remained. Each item is scored on a 5-point Likert scale (0 = Never/Not at all; 4 = All the time/Extremely). The final instrument includes the following domains and representative content:

Domain	Representative Items	Scoring Schema
Breadth of GenAI use	Use of different GenAIs; Different uses of GenAIs	Likert: 0–4
Orchestration competence	Confidence in one’s ability to use GenAIs	Likert: 0–4
Ethical/critical awareness	Awareness of GenAI limitations; Ethics of GenAI use	Likert: 0–4
Sociotechnical engagement	Engagement with online GenAI communities; News consumption	Likert: 0–4

Included items traverse practical expertise (e.g., browser extension adoption), critical differentiation (e.g., distinguishing GenAI outputs from search-engine results), and impact assessments (e.g., “Impact of GenAIs on one’s thinking”).

3. Factor Structure and Psychometric Properties

Exploratory factor analysis (EFA, maximum likelihood) yielded a primary eigenvalue $\lambda_1 \approx 7.27$ (accounting for 56% of variance) with a negligible secondary eigenvalue ($\lambda_2 \approx 0.49$, 4%), and a pronounced scree-plot elbow, confirming unidimensionality. Confirmatory factor analysis (CFA) with both ML and WLSMV estimation produced consistent standardized loadings ($\lambda \in [0.74, 0.89]$), minimal local residual dependencies ($|r_{resid}| \leq 0.12$), and fit indices: $\chi^2 = 2558.8$, $df = 39$, $RMSEA = 0.0988$, $CFI = 0.9243$, $TLI = 0.9092$, $SRMR = 0.0451$. Localized fit improvement upon allowing specific residual covariances confirmed structural unidimensionality with minor correlated errors, rather than evidence for multidimensionality (Souza, 21 Dec 2025).

4. Reliability and Measurement Invariance

Internal consistency of SMS is excellent, with Cronbach’s $\alpha \approx 0.94$ and average inter-item $r \approx 0.55$ across all cohorts. McDonald’s $\omega$ was not explicitly reported, but the homogeneous loadings imply $\omega \approx \alpha$. Definitions:

• Cronbach's $\alpha$:

$$\alpha = \frac{k}{k-1}\Bigl(1 - \frac{\sum_{i=1}^k\sigma_i^2}{\sigma^2_{\text{total}}}\Bigr)$$

with $k=13$ items.

Multi-group CFA across years established configural, metric, and partial scalar invariance after freeing intercepts for three items ($\Delta CFI = -0.0037$), indicating stability and comparability over time. Latent mean growth (+0.31 in 2024, +0.52 in 2025; 2023=0 reference) supports authentic cohort-level shifts in Sophotechnic Mediation.

5. Distributional Characteristics and Generative Modeling

Raw SMS scores manifest as a mixture: exact zeros (non-adopters) and positive values (adopters). Over three years, zero-mass declines (10.5%→4.8%); among adopters, mean increases (0.29→0.42), SD remains stable, and distribution approaches Gaussianity (Kolmogorov–Smirnov $D = 0.083 \rightarrow 0.033$).

A single Tobit model (left-censored normal) was inappropriate, whereas a two-process hurdle model yielded stable, superior fits ($\Delta$AIC $\gg$ 0, decisive likelihood-ratio for year effect):

• Adoption process: $\pi_i = \Pr(y_i>0) = \mathrm{logit}^{-1}(X_i\beta)$
• Intensity among adopters:

$$\Pr(y_i=y\mid y>0)=\frac{\phi\big(\frac{y-\mu_i}{\sigma}\big)}{\sigma[1-\Phi(-\frac{\mu_i}{\sigma})]}$$

with $\phi, \Phi$ as standard normal PDF and CDF. Year impacts adoption probability and mean mediation intensity independently. These findings instantiate a diffusion-of-innovation process: logistic adoption and truncated-normal intensity evolve distinctly until population-wide normality is approximated.

6. Nomological Network and Moderators of Development

Stepwise regression demonstrates cumulative GenAI experience ($\beta \approx .45$, unique $R^2 \approx 17.0\%$) and Hypercultural mediation ($\beta \approx .31$, $R^2 \approx 6.7\%$) as primary SMS predictors. Lesser effects arise from scientific training ($\beta \approx .11$), general T&D ($\beta \approx .09$), IQ ($\beta \approx .05$), male sex ($\beta \approx .04$), with negative contributions from Conscientiousness ($\beta \approx -$.09), Neuroticism ($\beta \approx -$.04$), and age ($\beta \approx -$.08$).

Developmental analysis reveals age-experience interactions: users beginning GenAI at age ≤25 show higher initial SMS at low exposure but are surpassed after ~12 months by those starting at 26–35 or ≥36, who attain higher long-term mediation levels. Items indexing community engagement, extension adoption, and GenAI news consumption display similar crossover effects. High-Hyperculture status confers a uniform mediation advantage, supporting additive (not compensatory) relationships between prior digital mediation and Sophotechnia.

7. Distinction from Related Constructs and Empirical Significance

Sophotechnic Mediation is empirically and psychometrically distinct from Hypercultural mediation. It emerges as a coherent, scalable mode of internalized cognitive mediation specific to generative AI, driven largely by cumulative experience and temporally modulated by age. SMS satisfies criteria for psychometric rigor: structural unidimensionality with localized dependencies, cross-cohort reliability, separate generative mechanisms for adoption and mediation level, and clear embedding in a nomological network.

These findings substantiate CMNT’s predictions regarding the transformative cognitive impact of GenAI integration and provide a robust foundation for ongoing comparative, longitudinal, and experimental investigations of mediation regimes in technologically saturated sociocognitive environments (Souza, 21 Dec 2025).