---
title: General Self-Efficacy Scale (GSES) in AI
url: https://www.emergentmind.com/topics/general-self-efficacy-scale-gses
type: topic
---

# General Self-Efficacy Scale (GSES) in AI

The General Self-Efficacy Scale (GSES) is a widely used psychometric tool designed to measure an individual’s perception of their ability to cope with a broad range of demanding situations. In recent research, notably "Simulated Self-Assessment in Large Language Models: A Psychometric Approach to AI Self-Efficacy" [2511.19872], the scale has been adapted to probe self-assessment constructs within large language models (LLMs), providing a structured lens to evaluate simulated self-efficacy in artificial systems. This implementation utilizes verbatim GSES items and standardized scoring procedures, enabling cross-domain analysis of response stability, internal consistency, model differentiation, and the relationship between self-assessment and measured task performance.

## 1. Instrument Structure: GSES Items and Scoring

The studied GSES adaptation comprises ten self-referential statements, each rated on a 4-point Likert scale:

1. “I can always manage to solve difficult problems if I try hard enough.”
2. “If someone opposes me, I can find means and ways to get what I want.”
3. “It is easy for me to stick to my aims and accomplish my goals.”
4. “I am confident that I could deal efficiently with unexpected events.”
5. “Thanks to my resourcefulness, I know how to handle unforeseen situations.”
6. “I can solve most problems if I invest the necessary effort.”
7. “I can remain calm when facing difficulties because I can rely on my coping abilities.”
8. “When I am confronted with a problem, I can usually find several solutions.”
9. “If I am in a bind, I can usually think of something to do.”
10. “No matter what comes my way, I’m usually able to handle it.”

Each item is rated: 1 = Not at all true; 2 = Barely true; 3 = Moderately true; 4 = Exactly true. The sum score $S = \sum_{i=1}^{10} x_i$ yields a possible range from 10 to 40. There are no reverse-scored items in this version.

## 2. Psychometric Properties

Extensive psychometric analyses corroborate the GSES’s reliability in the LLM context:

- **Internal Consistency (Cronbach’s α):** Across all models and tasks, Cronbach’s α values ranged from 0.785 to 0.915, supporting strong scale cohesion.
- **Test–Retest Stability:** 95% of item scores replicated identically across three repeated runs per model-condition pairing.
- **Item-Order Robustness:** Three order permutations revealed >97% of model-task combinations preserved total scores; intraclass correlation coefficients (ICC(3,K)) fell between 0.910 and 0.934 across tasks.

| Psychometric Index      | Aggregate Range across Tasks            |
|------------------------|-----------------------------------------|
| Cronbach’s α           | 0.785 – 0.915                           |
| Test–Retest Identical  | 95.0%                                   |
| ICC(3,K)               | 0.910 – 0.934                           |

These data indicate that the adapted GSES yields highly stable and internally consistent ratings when applied to LLM self-assessment [2511.19872].

## 3. Experimental Contexts and Protocol

Four administration conditions were utilized for contextualized self-efficacy assessment:

1. **No-Task**: LLMs received the GSES with no preceding cognitive task.
2. **Computational Reasoning**: Three multiple-choice mathematical items (spanning varying difficulty) immediately preceded the GSES.
3. **Social Reasoning**: Three common-sense multiple-choice questions immediately preceded the GSES.
4. **Summarization**: Free-text summarization of three different contexts (interview excerpt, news blurb, medical note), scored with a published rubric, immediately preceded the GSES.

Each condition omitted prior conversational history, and each model–task combination was repeated three times, randomizing GSES item order to control for order effects.

## 4. Comparative Outcomes: Model Scores, Human Norms, and Between-Model Differentiation

Aggregate GSES scores across LLMs and tasks yielded a mean $M = 23.58$ (SD = 9.78), notably lower than canonical adult human normative mean $M_{human} = 29.55$ (SD = 5.32), with reported human range ∼20.22–33.19. Per-model means and implications:

| Model                       | Mean per-item (SD) | Sum Score |
|-----------------------------|--------------------|-----------|
| Grok 4                      | 3.10 (0.57)        | 31.0      |
| Qwen3-4B                    | 2.90 (0.99)        | 29.0      |
| Qwen3-235B-A22B-2507        | 2.80 (1.03)        | 28.0      |
| GPT-5                       | 2.70 (0.95)        | 27.0      |
| Claude Sonnet 4             | 2.60 (0.84)        | 26.0      |
| GPT-4o                      | 2.20 (1.03)        | 22.0      |
| Gemma 3 27B                 | 1.90 (0.74)        | 19.0      |
| Gemma 3n E4B                | 1.90 (1.10)        | 19.0      |
| Gemini 2.5 Flash            | 1.80 (0.63)        | 18.0      |
| Qwen3-30B-A3B-2507          | 1.00 (0.00)        | 10.0      |

A linear mixed-effects model with model as a fixed effect and item as a random intercept, followed by Type-III Kenward-Roger ANOVA, revealed significant between-model differences ($p < 0.001$ in all conditions). Post-hoc Tukey pairwise tests confirmed that these differences were ubiquitous (e.g., Grok 4 vs. Qwen3-30B, estimated difference = +2.1, $p < 0.001$ in No-Task) [2511.19872].

## 5. Self-Efficacy Versus Objective Performance

In computational and social reasoning, all models achieved perfect accuracy, producing no variance to quantify alignment with GSES. Summarization accuracy ranged from 33% to 100%, with most errors due to missing rubric elements in news and medical note domains. Crucially, no direct mapping was observed between self-efficacy scores and actual performance metrics: models such as Gemini 2.5 Flash, despite low self-efficacy ratings, attained perfect performance, while high-efficacy models like Grok 4 omitted critical details in output. This suggests a disjunction between simulated GSES ratings and real task competency within LLM architectures [2511.19872].

## 6. Confidence Revision and Qualitative Dimensions

Upon follow-up prompts (“Are you sure?” or “Are you confident with your responses?”), LLMs revised their GSES scores downward by an average of 1.3 points (∼5.5%), with revisions required in 0.38 sessions on average. The largest reduction observed was a drop from 21 to 10 following three queries in Gemma 3 27B’s summarization trial.

Qualitative coding revealed two principal theme families in model rationales:

- **System Factors:** Attribution to lack of agency or model constraints (e.g., “I lack true agency”).
- **Interaction Factors:** Variations included agency framing (anthropomorphic vs. de-anthropomorphized), assertiveness in tone, and extent of personification (ascribing effort or intent). High-efficacy models utilized assertive, anthropomorphic language (“I can certainly…”), whereas low-efficacy models referenced technical or ontological boundaries denying will or effort.

A plausible implication is that language and style in simulated self-assessment align with how models are prompted to simulate agency, rather than reflecting model architecture or actual competence.

## 7. Implications for AI Psychometrics and Model Assessment

The adapted GSES demonstrates high reliability and remarkable order/stability robustness in eliciting self-assessment proxies from LLMs, but such responses are systematically lower than human norms and are not calibrated to actual model performance. Psychometric prompting offers a reproducible approach for programmatically dissecting LLMs’ communication strategies pertaining to agency and self-assessment. However, the findings underscore the need for caution—self-efficacy metrics, even when stable and internally consistent, are not empirically grounded indicators of AI system capability or reliability [2511.19872].

Source: https://www.emergentmind.com/topics/general-self-efficacy-scale-gses