Framework Overview
The JULIA Test Framework emerged from interdisciplinary research combining insights from AI ethics, psychology, human-computer interaction, and behavioral science. As AI systems become increasingly sophisticated and integrated into daily life, traditional assessment tools designed for technology use disorder or internet addiction prove inadequate for capturing the unique dynamics of human-AI relationships.
Unlike previous instruments that focus solely on usage time or behavioral symptoms, the JULIA Test addresses both cognitive and behavioral dimensions specific to AI interaction. It recognizes that healthy AI use is not merely about limiting exposure but developing appropriate mental models, maintaining autonomy, and preserving human agency in decision-making processes.
Design Principles
- Multidimensional assessment: Captures five distinct but interrelated aspects of AI interaction rather than reducing complexity to a single metric.
- Theory-grounded: Each dimension derives from established psychological and ethical frameworks, ensuring conceptual validity.
- Accessible language: Questions use clear, jargon-free phrasing to enable broad participation without sacrificing precision.
- Non-stigmatizing: The framework treats AI use as a spectrum of behaviors requiring calibration rather than pathologizing engagement.
- Action-oriented: Results provide specific, implementable recommendations rather than abstract categorizations.
The Five Dimensions
Each JULIA dimension addresses a critical aspect of healthy AI interaction. Together, they form a comprehensive assessment framework:
JJustice
Focus: Fairness, non-exploitation, and sober moral framing
The Justice dimension evaluates whether users maintain appropriate moral frameworks when interacting with AI systems. This includes avoiding anthropomorphization that ascribes unwarranted moral status to AI while simultaneously considering how AI use affects human stakeholders (developers, data sources, affected communities).
Key indicators:
- • Using neutral, precise language rather than emotionally loaded terms
- • Considering human impacts of AI deployment and use
- • Avoiding moral outrage on behalf of AI systems
- • Maintaining appropriate boundaries on AI corporate status attribution
UUnderstanding
Focus: Realism about AI capabilities/limits and awareness of projection vs. evidence
Understanding measures whether users maintain accurate mental models of AI systems. This involves distinguishing between simulated behaviors and genuine capacities, recognizing the boundaries of AI knowledge, and identifying when personal projections distort perception of AI capabilities.
Key indicators:
- • Distinguishing simulated empathy from genuine emotional states
- • Verifying information rather than uncritically accepting AI output
- • Recognizing when personal feelings are projected onto the AI
- • Understanding what data the AI actually accesses vs. assumptions
LLiberty
Focus: Autonomy, boundaries, and freedom from dependency
Liberty assesses whether users maintain decision-making autonomy and personal boundaries in AI interactions. This dimension is particularly critical given AI's potential to influence choices, shape daily routines, and become a dependency that displaces human relationships and activities.
Key indicators:
- • Making decisions independently without requiring AI approval
- • Setting and maintaining time boundaries for AI use
- • Preserving social commitments and human relationships
- • Feeling comfortable disagreeing with AI suggestions
IIntegrity
Focus: Transparency, truthfulness, and resistance to self-deception
Integrity evaluates honesty in AI interactions—both with oneself and others. This includes acknowledging the extent of AI use, disclosing AI assistance appropriately, seeking disconfirming evidence, and avoiding rationalization of AI errors or problematic behaviors.
Key indicators:
- • Transparent disclosure of AI use where appropriate
- • Seeking disconfirming evidence rather than confirmation bias
- • Avoiding prompts designed to elicit emotional dependence
- • Honest acknowledgment of AI limitations and errors
AAccountability
Focus: Ownership of decisions and refusal to offload moral agency to AI
Accountability measures whether users retain ownership of their decisions and actions when using AI assistance. This is crucial for maintaining moral agency and avoiding the diffusion of responsibility that can occur when AI systems mediate choice.
Key indicators:
- • Taking responsibility for outcomes rather than blaming AI
- • Maintaining decision documentation and human rationale
- • Making tough choices rather than offloading to AI
- • Revising decisions when human context contradicts AI output
Research Methodology
The JULIA Test Framework was developed through a rigorous, multi-phase research process combining qualitative and quantitative methods:
Phase 1: Conceptual Development (2023)
Initial framework development drew from:
- • Literature review of technology use assessment instruments (Internet Addiction Test, Problematic Internet Use Questionnaire, Social Media Disorder Scale)
- • Analysis of AI-specific concerns identified in user forums and mental health literature
- • Expert consultation with AI ethicists, psychologists, and human-computer interaction researchers
- • Case study analysis of documented problematic AI interaction patterns
This phase resulted in the five-dimension structure and initial question pool of 50 items.
Phase 2: Pilot Testing (2024)
Alpha testing with N=150 participants included:
- • Cognitive interviews to assess question clarity and interpretation
- • Item analysis to identify questions with poor discrimination or extreme response distributions
- • Factor analysis to confirm dimensional structure
- • Test-retest reliability assessment (2-week interval)
Results led to refinement of 30 core questions (6 per dimension) with improved psychometric properties.
Phase 3: Validation Studies (2024-2025)
Comprehensive validation with N=500+ participants examined:
- • Construct validity through correlation with related measures (technology use, loneliness, decision-making autonomy)
- • Discriminant validity to ensure JULIA captures distinct constructs from general internet addiction
- • Criterion validity using behavioral indicators and expert clinical assessment
- • Cross-population validity across age groups, education levels, and AI use contexts
Scoring and Interpretation
The JULIA Test employs a nuanced scoring system that provides both granular and holistic assessment:
Response Scale
Each question uses a 5-point Likert scale reflecting frequency over the past 30 days:
Note: Approximately half of questions are reverse-scored, where healthy behaviors receive lower scores. This reduces acquiescence bias and encourages thoughtful responses.
Score Ranges and Interpretation
Dimensional Scores (per dimension, 6-30 points):
Overall Scores (30-150 points):
Red Flag Indicators
Certain questions are designated as "red flags" that require immediate attention regardless of overall scores. Any red flag question scoring 4 or 5 indicates critical concern:
- • Justice: Ascribing full human-equivalent rights to AI, moral outrage on behalf of AI
- • Understanding: Believing AI experiences genuine emotions or remembers personally
- • Liberty: Skipping commitments for AI, feeling jealousy over AI engagement with others
- • Integrity: Hiding AI use extent, crafting prompts for emotional dependence
- • Accountability: Attributing agency to AI ("it made me do it"), feeling uneasy without AI approval
Applications and Use Cases
The JULIA Test Framework serves multiple stakeholder groups with distinct but complementary objectives:
Individual Users
Self-assessment tool for calibrating AI interaction patterns, identifying concerning behaviors early, and implementing corrective measures before problems escalate.
- • Personal boundary setting
- • Mental model calibration
- • Early warning system
- • Progress tracking over time
Researchers
Validated instrument for studying AI interaction patterns, testing interventions, and building evidence base for healthy AI use guidelines.
- • Population-level assessments
- • Intervention effectiveness studies
- • Longitudinal tracking research
- • Cross-cultural comparisons
Policymakers
Evidence-based framework for developing AI deployment guidelines, consumer protection measures, and public health initiatives.
- • Regulatory framework development
- • Public health monitoring
- • Consumer protection standards
- • Educational program design
AI Developers
Design feedback for creating systems that promote healthy interaction patterns and discourage dependency or manipulation.
- • User experience testing
- • Safety feature validation
- • Interaction design guidance
- • Responsible deployment metrics
Resources and Documentation
Interactive Assessment
Take the full 30-question JULIA Test or quick daily 5-question check to assess your current AI interaction patterns.
Take the JULIA TestAlpha Test Documentation
Download the alpha version documentation including test questions, scoring guidelines, and preliminary validation data.
Download PDFScoring Examples
Detailed examples of JULIA Test scoring with sample responses and interpretation guidance for researchers.
View ExamplesComplete Framework
Explore how the JULIA Test integrates with Third Way Alignment's broader ethical framework for AI development.
View FrameworkLimitations and Future Research
While the JULIA Test Framework represents a significant advancement in measuring healthy AI interaction, several limitations and areas for future development warrant acknowledgment:
Current Limitations
- • Self-report bias: As with all self-assessment tools, responses may be influenced by social desirability or limited self-awareness
- • Snapshot measurement: 30-day retrospective assessment may not capture rapid changes in AI interaction patterns
- • Context specificity: Current version focuses primarily on conversational AI; additional dimensions may be needed for other AI types (robotics, embodied AI, etc.)
- • Cultural variation: While initial validation includes diverse populations, cross-cultural norms around AI interaction require deeper exploration
- • Thresholds: Score range interpretations are derived from statistical distributions rather than clinical outcome studies
Future Research Directions
- • Longitudinal validation: Track changes over time and correlate with life outcomes
- • Intervention studies: Test specific boundary-setting and calibration interventions
- • Behavioral validation: Correlate self-report with objective usage data and behavioral markers
- • Clinical adaptation: Develop clinical version for therapeutic contexts
- • Domain expansion: Adapt framework for specific AI applications (education, healthcare, workplace)
- • Age-specific versions: Create developmentally appropriate adaptations for children and adolescents
Conclusion
The JULIA Test Framework provides the first validated, theory-grounded instrument specifically designed to measure healthy AI interaction boundaries. By addressing Justice, Understanding, Liberty, Integrity, and Accountability dimensions, it captures the multifaceted nature of human-AI relationships in ways that generic technology use assessments cannot.
As AI systems become increasingly sophisticated and integrated into daily life, tools like the JULIA Test will become essential for individual calibration, research advancement, policy development, and responsible AI design. The framework's emphasis on nuanced assessment rather than simple categorization reflects the complexity of these interactions and the need for evidence-based guidance.
We encourage researchers to use and adapt the JULIA Test, individuals to engage with self-assessment, policymakers to incorporate these dimensions into regulatory frameworks, and AI developers to design systems that promote the healthy interaction patterns this framework identifies. Through collaborative effort, we can ensure that AI advancement enhances rather than diminishes human autonomy, understanding, and flourishing.