Theories & Measures
A curated repository of theories, frameworks, and measurement scales used and recommended by the ASCI Lab.
A living resource maintained by the ASCI Lab. We draw on theories and measures from cognitive science, social psychology, HCI, and AI research. This page is updated as we encounter and use new frameworks in our work.
Theories & Frameworks
Technology Acceptance Model (TAM)
A foundational model explaining how users come to accept and use technology, centred on perceived usefulness and perceived ease of use. Widely applied in human-AI interaction research to understand adoption and engagement.
Social Identity Theory
Proposes that individuals derive part of their self-concept from membership in social groups. Relevant to understanding how group dynamics, identity, and intergroup processes shape behaviour and decision-making in human-AI contexts.
Dual Process Theory
Distinguishes between fast, automatic System 1 thinking and slow, deliberate System 2 reasoning. Highly relevant to understanding how people process AI recommendations and explanations under varying cognitive load and time pressure.
Situational Awareness
A framework describing the perception, comprehension, and projection of environmental elements within a volume of time and space. Applied in human-AI teaming research to understand how AI assistance affects operators' awareness of task states.
Measurement Scales
Trust in Automation Scale
A widely used 12-item scale measuring trust and distrust in automated systems. Captures dimensions including reliability, dependability, faith, and suspicion. Commonly used in human-AI decision-making studies.
NASA Task Load Index (NASA-TLX)
A multidimensional scale assessing perceived workload across six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration. Frequently used to assess cognitive load in AI-assisted task studies.
Reliance Measures in AI-Assisted Decision-Making
Operationalises appropriate reliance through behavioural metrics including relative positive AI reliance (RPAR) and relative positive self-reliance (RPSR), distinguishing appropriate from inappropriate agreement with AI recommendations.
System Usability Scale (SUS)
A simple, reliable 10-item scale for measuring usability of interactive systems. Produces a single score from 0–100 representing overall usability. A standard benchmark in HCI and interaction design research.