1 Abstract

The Comprehensive Math Anxiety Assessment Scale (CMAAS) represents a paradigm shift in the measurement of mathematics anxiety, moving beyond surface-level symptoms to target specific underlying neurobiological, cognitive, and behavioral mechanisms. This paper documents the development of a 56-item multidimensional scale assessing math anxiety across seven theoretically grounded domains: cognitive worry, affective distress, physiological arousal, behavioral patterns, value beliefs, self-efficacy, and social-cultural processes. Using a novel mechanism-based AI-assisted approach, we integrated findings from cognitive neuroscience, affective science, and behavioral psychology into the item generation process. Each item was designed to map onto specific mechanisms (e.g., prefrontal cortex resource depletion, default mode network hyperactivity, sympathetic nervous system activation). The AIGENIE R package interfaced with local large language models via Ollama to generate items that capture both adaptive and maladaptive aspects of these mechanisms. The scale includes parallel forms (A and B) for repeated measures designs, comprehensive mechanism-to-item mapping, and an interactive Shiny dashboard for item review. This document serves as both a research publication and a template for mechanism-based AI-assisted scale development.

2 Introduction

2.1 Theoretical Foundations of Math Anxiety

Mathematics anxiety, defined as "a feeling of tension and anxiety that interferes with the manipulation of numbers and the solving of mathematical problems in a wide variety of ordinary life and academic situations" (Richardson & Suinn, 1972), affects approximately 20-25% of students and adults in developed countries (Luttenberger et al., 2018). The consequences of math anxiety extend beyond academic performance to career choices, financial decision-making, and overall quality of life (Beilock & Maloney, 2014).

2.2 Historical Context

Early conceptualizations of math anxiety focused primarily on affective responses to mathematical situations. The Mathematics Anxiety Rating Scale (MARS) (Suinn et al., 1972), one of the first systematic measures, emphasized situational anxiety across academic and everyday contexts. Subsequent measures, such as the Abbreviated Math Anxiety Scale (AMAS) (Hopko et al., 2003), provided shorter alternatives but maintained a primarily unidimensional or bidimensional structure.

2.3 The Multidimensional Nature of Math Anxiety

Contemporary research has revealed that math anxiety is a complex phenomenon involving multiple interacting systems:

Cognitive Components: Working memory interference (Beilock, 2007), attentional biases (Hopko et al., 2004), and negative self-talk (Lyons & Beilock, 2014) characterize the cognitive dimension of math anxiety. These cognitive processes consume limited attentional resources, creating a performance-impairing cycle.

Affective Components: Beyond simple anxiety, math-anxious individuals experience shame (Turner & Meyer, 2002), frustration (Kim & Kim, 2015), and emotional dysregulation (Goetz et al., 2013) when confronting mathematical tasks.

Physiological Components: Neuroendocrine responses (Mattarella-Micke & Beilock, 2017), autonomic activation (Quaid & Lyons, 2015), and somatic symptoms (Lyons & Beilock, 2012) represent the physiological manifestation of math anxiety.

Behavioral Components: Avoidance behaviors (Ashcraft & Krause, 2007), safety-seeking (Maloney & Beilock, 2013), and reduced persistence (Hoffman, 2010) characterize the behavioral dimension.

Motivational Components: Value beliefs (Wigfield & Eccles, 2000), cost perceptions (Flake et al., 2015), and interest (Renninger & Hidi, 2015) influence engagement with mathematical content.

Self-Perception Components: Math self-efficacy (Pajares & Miller, 2003), competence beliefs (Marsh & Martin, 2006), and attributional style (Weiner, 2010) shape individuals' responses to mathematical challenges.

Social-Cultural Components: Social comparison (Festinger, 1954), identity processes (Schoenfeld, 2016), cultural transmission (Stigler & Hiebert, 2004), and vicarious learning (Bandura, 1977) contextualize math anxiety within broader social frameworks.

2.4 The Need for Comprehensive Assessment

Existing measures, while valuable, have several limitations:

Limited domain coverage - Most measures focus on affective and cognitive components, neglecting physiological, behavioral, and social-cultural dimensions
Inadequate representation of adaptive processes - Many scales focus exclusively on pathology, missing resilience factors
No parallel forms - Researchers studying interventions lack equivalent forms for repeated measures
Static item pools - Traditional development methods are time-consuming and difficult to update

2.5 AI-Assisted Item Generation: A Methodological Innovation

Recent advances in natural language processing and large language models (LLMs) offer new possibilities for item generation. The AIGENIE package (Team, 2023) provides a bridge between psychological measurement and generative AI, enabling:

Rapid generation of large item pools
Systematic manipulation of item attributes
Control over linguistic complexity and content
Reproducible item development processes

2.6 Ethical Considerations in AI-Assisted Assessment

While AI-assisted item generation offers efficiency gains, several ethical considerations must be addressed:

Bias mitigation: AI models may perpetuate existing biases in training data
Content validity: Generated items must align with theoretical frameworks
Expert review: AI-generated items require human oversight and evaluation
Transparency: Clear documentation of generation procedures is essential

3 Method

3.1 Study Design

This scale development study employed a mixed-methods approach combining:

Theoretical specification: Domain and facet definitions based on comprehensive literature review
AI-assisted item generation: Systematic item creation using local LLMs
Mechanism mapping: Each generated item linked to its target mechanism
Expert review: Quality assessment and refinement

3.2 Mechanism-Based AI-Assisted Item Generation

3.2.1 Custom Prompt Function with Mechanism Integration

# Function to generate mechanism-specific prompts
generate_mechanism_prompt <- function(domain, facet, mechanism, adaptive_example, maladaptive_example, n_items = 2) {
  
  prompt <- paste0(
    'Generate ', n_items * 2, ' items measuring ', facet, ' in ', domain, 
    ' for a mathematics anxiety scale.\n\n',
    'TARGET MECHANISM:\n',
    mechanism, '\n\n',
    'The items should be designed such that endorsement reflects the operation of this specific mechanism.\n\n',
    'EXAMPLE ITEMS THAT SUCCESSFULLY TARGET THIS MECHANISM:\n',
    '- Adaptive (low anxiety, reflects successful regulation): "', adaptive_example, '"\n',
    '- Maladaptive (high anxiety, reflects mechanism dysfunction): "', maladaptive_example, '"\n\n',
    'Generate ', n_items, ' adaptive items and ', n_items, ' maladaptive items that:\n',
    '1. Directly reflect the specified mechanism in everyday language\n',
    '2. Use first-person statements ("I" statements)\n',
    '3. Are 30-150 characters in length\n',
    '4. Are understandable to general audiences (reading level appropriate for adolescents and adults)\n',
    '5. Avoid double-barreled questions\n',
    '6. Capture different aspects or manifestations of the mechanism\n\n',
    'Response format: 5-point Likert from 1 = "Strongly Disagree" to 5 = "Strongly Agree"\n',
    'Audience: adults and adolescents in educational settings'
  )
  
  return(prompt)
}

# Test the function with an example
test_prompt <- generate_mechanism_prompt(
  domain = "cognitive_worry",
  facet = "working_memory_interference",
  mechanism = "Prefrontal cortex resource depletion — anxiety diverts executive resources from mathematical processing [Eysenck et al., 2012]",
  adaptive_example = "I can hold multiple steps in mind while solving math problems",
  maladaptive_example = "My mind goes blank when I try to keep track of math steps"
)

cat("Example mechanism-based prompt:\n")

## Example mechanism-based prompt:

cat(strtrim(test_prompt, 500), "...\n")

## Generate 4 items measuring working_memory_interference in cognitive_worry for a mathematics anxiety scale.
## 
## TARGET MECHANISM:
## Prefrontal cortex resource depletion — anxiety diverts executive resources from mathematical processing [Eysenck et al., 2012]
## 
## The items should be designed such that endorsement reflects the operation of this specific mechanism.
## 
## EXAMPLE ITEMS THAT SUCCESSFULLY TARGET THIS MECHANISM:
## - Adaptive (low anxiety, reflects successful regulation): "I can hold multiple steps in mind whi ...

3.2.2 Generating Items by Domain

3.2.2.1 Prerequisites: Setting Up Ollama

Before running the item generation, ensure Ollama is installed and running with the required model:

# This chunk checks if Ollama is running - it won't execute during knitting
# Run this manually in your R console before knitting

# Check if Ollama is accessible
ollama_running <- tryCatch({
  readLines("http://localhost:11434/api/tags", warn = FALSE)
  TRUE
}, error = function(e) FALSE)

if(!ollama_running) {
  message("⚠️ Ollama is not running! Please:")
  message("1. Open a terminal")
  message("2. Run: ollama serve")
  message("3. In another terminal, run: ollama pull mistral")
  message("4. Then try again")
} else {
  # Check if mistral model is available
  models <- jsonlite::fromJSON("http://localhost:11434/api/tags")
  if(!"mistral" %in% models$models$name) {
    message("⚠️ Mistral model not found. Run in terminal: ollama pull mistral")
  } else {
    message("✅ Ollama is running with mistral model available")
  }
}

Setup Instructions:

Install Ollama from ollama.ai
Start the Ollama server in a terminal:

ollama serve

Pull the mistral model (in another terminal):

ollama pull mistral

Verify it's working:

curl http://localhost:11434/api/tags

Keep Ollama running in the background while you knit this document

The item generation will fail if Ollama is not running, so make sure to complete these steps first.

#===============================================================================
# MECHANISM-DRIVEN ITEM GENERATION FOR CMAAS
# Hybrid approach: Multiple AIGENIE calls with mechanism-specific prompts
#===============================================================================

library(AIGENIE)
library(tidyverse)
library(jsonlite)

#===============================================================================
# STEP 1: Define All Mechanisms with Examples
#===============================================================================

mechanism_library <- list(
  
  #=============================================================================
  # COGNITIVE DOMAIN
  #=============================================================================
  cognitive = list(
    
    negative_thoughts = list(
      name = "Default Mode Network Hyperactivity",
      description = "Self-referential, anxiety-linked rumination during task engagement [Raichle et al., 2001]",
      adaptive_example = "I can quiet my mind and focus when solving math problems",
      maladaptive_example = "Worrying thoughts keep interrupting me when I try to do math",
      key_phrases = c("worrying thoughts", "mind wandering", "can't stop thinking about")
    ),
    
    working_memory = list(
      name = "Prefrontal Cortex Resource Depletion",
      description = "Anxiety-driven diversion of attentional control, reducing effective executive function [Eysenck et al., 2012]",
      adaptive_example = "I can hold multiple steps in mind while solving math problems",
      maladaptive_example = "My mind goes blank when I try to keep track of math steps",
      key_phrases = c("mind goes blank", "can't hold steps", "forget what I'm doing")
    ),
    
    catastrophic_thinking = list(
      name = "Amygdala–Prefrontal Dysregulation",
      description = "Overactivation of fear-processing regions, underactivation of regulatory control [Phelps, 2004]",
      adaptive_example = "I can think clearly about math without imagining worst-case scenarios",
      maladaptive_example = "One small mistake in math makes me fear I'll fail entirely",
      key_phrases = c("worst-case", "catastrophe", "everything will go wrong")
    ),
    
    attentional_deficits = list(
      name = "Attentional Control Theory",
      description = "Anxiety impairs goal-directed attentional system and enhances stimulus-driven attention [Eysenck et al., 2007]",
      adaptive_example = "I can maintain my focus throughout math tasks",
      maladaptive_example = "My attention wanders constantly when I'm working on math",
      key_phrases = c("attention wanders", "can't concentrate", "easily distracted")
    )
  ),
  
  #=============================================================================
  # AFFECTIVE DOMAIN
  #=============================================================================
  affective = list(
    
    anticipatory_anxiety = list(
      name = "Limbic System Reactivity",
      description = "Overactivation of fear- and threat-processing regions [LeDoux, 1996]",
      adaptive_example = "I feel calm when thinking about upcoming math work",
      maladaptive_example = "I feel a sense of dread the day before a math class",
      key_phrases = c("dread", "anticipate", "days before")
    ),
    
    frustration = list(
      name = "Cognitive Reappraisal Capacity",
      description = "Ability to reframe challenging tasks as opportunities for growth [Gross, 2002]",
      adaptive_example = "I can work through challenging math without getting upset",
      maladaptive_example = "I get fed up easily when I can't understand math concepts",
      key_phrases = c("fed up", "frustrated", "irritated")
    ),
    
    shame = list(
      name = "Anterior Cingulate Cortex Activation",
      description = "Social-evaluative pain circuitry activated by math struggle [Eisenberger et al., 2003]",
      adaptive_example = "I can ask for help in math without feeling embarrassed",
      maladaptive_example = "I feel humiliated when I struggle with math in front of others",
      key_phrases = c("humiliated", "embarrassed", "ashamed", "in front of others")
    ),
    
    emotional_dysregulation = list(
      name = "Dual-Process Emotion Dysregulation",
      description = "Impaired cognitive control over emotional responses [Gross, 2015]",
      adaptive_example = "I can stay calm and in control during math activities",
      maladaptive_example = "Math activities make me feel emotionally overwhelmed",
      key_phrases = c("overwhelmed", "can't control emotions", "emotional")
    )
  ),
  
  #=============================================================================
  # PHYSIOLOGICAL DOMAIN
  #=============================================================================
  physiological = list(
    
    autonomic_activation = list(
      name = "Sympathetic Nervous System Activation",
      description = "Fight-or-flight cardiovascular response to perceived threat [Sapolsky, 2004]",
      adaptive_example = "My body stays calm during math tests",
      maladaptive_example = "My heart races when I'm called on in math class",
      key_phrases = c("heart races", "sweating", "shaking", "palpitations")
    ),
    
    cortisol_response = list(
      name = "HPA Axis Activation",
      description = "Cortisol elevation (15-30%) impairing hippocampal function [Lupien et al., 2007]",
      adaptive_example = "I can think clearly even during stressful math situations",
      maladaptive_example = "I have trouble sleeping the night before a math exam",
      key_phrases = c("can't sleep", "restless", "trouble sleeping")
    ),
    
    somatic_symptoms = list(
      name = "Neuromuscular Tension Feedback",
      description = "Sustained muscle activation signaling stress and perpetuating anxiety [Blascovich, 2000]",
      adaptive_example = "My body feels relaxed when doing math",
      maladaptive_example = "My shoulders and neck get tense when solving math problems",
      key_phrases = c("tense", "tight muscles", "stomach knots")
    ),
    
    fight_flight = list(
      name = "Acute Stress Cascade",
      description = "Adrenaline release, increased heart rate, behavioral inhibition [Gray, 1982]",
      adaptive_example = "I can stay seated and focused during math class",
      maladaptive_example = "I feel the urge to run away when faced with math problems",
      key_phrases = c("run away", "escape", "flee", "get out")
    )
  ),
  
  #=============================================================================
  # BEHAVIORAL DOMAIN
  #=============================================================================
  behavioral = list(
    
    active_avoidance = list(
      name = "Negative Reinforcement Cycle",
      description = "Avoidance temporarily reduces distress, reinforcing long-term withdrawal [Skinner, 1953]",
      adaptive_example = "I face math tasks directly even when they're challenging",
      maladaptive_example = "I avoid taking math classes whenever possible",
      key_phrases = c("avoid", "steer clear", "skip")
    ),
    
    procrastination = list(
      name = "Temporal Discounting Bias",
      description = "Preference for short-term relief over long-term benefit [Ainslie, 1975]",
      adaptive_example = "I start my math assignments with plenty of time",
      maladaptive_example = "I put off doing my math homework until the last minute",
      key_phrases = c("put off", "procrastinate", "last minute", "delay")
    ),
    
    safety_behaviors = list(
      name = "Superstitious Avoidance",
      description = "Short-term anxiety reduction prevents long-term fear extinction [Ferster & Skinner, 1957]",
      adaptive_example = "I try to solve math problems on my own before using tools",
      maladaptive_example = "I rely heavily on calculators even for simple math",
      key_phrases = c("calculator", "depend on", "can't without")
    ),
    
    task_persistence = list(
      name = "Effort Withdrawal Threshold",
      description = "Low tolerance for sustained engagement before disengagement [Eccles et al., 1998]",
      adaptive_example = "I stick with difficult math problems until I solve them",
      maladaptive_example = "I give up easily when math gets difficult",
      key_phrases = c("give up", "quit", "stop trying")
    )
  ),
  
  #=============================================================================
  # VALUE BELIEFS DOMAIN
  #=============================================================================
  value_beliefs = list(
    
    utility_value = list(
      name = "Constricted Future-Time Perspective",
      description = "Reduced motivation from failing to connect present effort with future benefit [Zimbardo, 1999]",
      adaptive_example = "I can see how math applies to my future goals",
      maladaptive_example = "I don't see how I'll ever use this math in real life",
      key_phrases = c("real life", "future", "useful", "apply")
    ),
    
    cost_perception = list(
      name = "Academic Self-Concept Formation",
      description = "Diminished integration of math ability into overall self-concept [Shavelson et al., 1976]",
      adaptive_example = "The effort I put into math is worth the benefits",
      maladaptive_example = "Math requires more time and effort than it's worth",
      key_phrases = c("worth it", "too much time", "not worth")
    ),
    
    interest_value = list(
      name = "Competence Motivation System",
      description = "Intrinsic drive to build skills and efficacy through practice [White, 1959]",
      adaptive_example = "I enjoy exploring mathematical concepts on my own",
      maladaptive_example = "I find math boring and uninteresting",
      key_phrases = c("boring", "interesting", "enjoy", "fascinating")
    ),
    
    attainment_value = list(
      name = "Possible Selves Framework",
      description = "Incorporation of math ability into envisioned future selves [Markus & Nurius, 1986]",
      adaptive_example = "Being good at math is important to who I am",
      maladaptive_example = "Doing well in math is not central to how I see myself",
      key_phrases = c("who I am", "identity", "part of me")
    )
  ),
  
  #=============================================================================
  # SELF-EFFICACY DOMAIN
  #=============================================================================
  self_efficacy = list(
    
    confidence = list(
      name = "Efficacy Calibration",
      description = "Accuracy of self-appraisal relative to actual capability [Bandura, 1997]",
      adaptive_example = "I feel confident in my math abilities",
      maladaptive_example = "I doubt my ability to solve math problems",
      key_phrases = c("confident", "doubt", "believe in")
    ),
    
    mastery_expectations = list(
      name = "Growth Mindset",
      description = "Belief that ability develops through effort [Dweck, 2008]",
      adaptive_example = "I believe I can improve my math skills with practice",
      maladaptive_example = "I expect to struggle with math no matter how hard I try",
      key_phrases = c("improve", "get better", "no matter what")
    ),
    
    self_appraisal = list(
      name = "Metacognitive Accuracy",
      description = "Ability to accurately assess one's own capabilities [Kruger & Dunning, 1999]",
      adaptive_example = "I accurately judge my math abilities",
      maladaptive_example = "I often underestimate my math abilities even when I do well",
      key_phrases = c("underestimate", "accurately judge", "know my abilities")
    ),
    
    attributional_style = list(
      name = "Explanatory Style",
      description = "Internal vs. external, stable vs. unstable attributions [Seligman, 1975]",
      adaptive_example = "When I succeed in math, it's because of my effort and ability",
      maladaptive_example = "When I do well in math, it's usually because the test was easy",
      key_phrases = c("because I tried", "luck", "easy test", "worked hard")
    )
  ),
  
  #=============================================================================
  # SOCIAL-CULTURAL DOMAIN
  #=============================================================================
  social_cultural = list(
    
    social_comparison = list(
      name = "Upward Social Comparison",
      description = "Comparing to higher-performing peers elevates cortisol by 23% [Huguet et al., 2009]",
      adaptive_example = "I focus on my own progress in math, not how I compare to others",
      maladaptive_example = "Watching classmates solve problems quickly makes me doubt my abilities",
      key_phrases = c("compared to others", "classmates", "others solve")
    ),
    
    identity_conflict = list(
      name = "Stereotype Threat",
      description = "Activation of negative stereotypes impairing performance [Steele, 1997]",
      adaptive_example = "I feel that math is for people like me",
      maladaptive_example = "I feel that math isn't for people like me",
      key_phrases = c("people like me", "not for me", "don't belong")
    ),
    
    vicarious_learning = list(
      name = "Modeling Effects",
      description = "Observations of peer struggle reduce self-efficacy twice as effectively as personal failure [Schunk, 1987]",
      adaptive_example = "Seeing others succeed in math motivates me",
      maladaptive_example = "Watching others struggle with math makes me anxious about it",
      key_phrases = c("watching others", "seeing peers", "others struggle")
    ),
    
    cultural_transmission = list(
      name = "Social Learning Theory",
      description = "Cultural messages about math shape individual appraisals [Bandura, 1977]",
      adaptive_example = "My cultural background values mathematical achievement",
      maladaptive_example = "I've absorbed cultural messages that math is only for gifted people",
      key_phrases = c("culture", "background", "messages about math")
    )
  )
)

#===============================================================================
# STEP 2: Flatten the mechanism library for iteration
#===============================================================================

flatten_mechanisms <- function(mechanism_library) {
  
  all_mechanisms <- list()
  
  for (domain_name in names(mechanism_library)) {
    domain <- mechanism_library[[domain_name]]
    
    for (mechanism_name in names(domain)) {
      mechanism <- domain[[mechanism_name]]
      
      # Create a unique ID
      mech_id <- paste(domain_name, mechanism_name, sep = "_")
      
      all_mechanisms[[mech_id]] <- list(
        id = mech_id,
        domain = domain_name,
        mechanism_key = mechanism_name,
        name = mechanism$name,
        description = mechanism$description,
        adaptive_example = mechanism$adaptive_example,
        maladaptive_example = mechanism$maladaptive_example,
        key_phrases = paste(mechanism$key_phrases, collapse = ", ")
      )
    }
  }
  
  return(all_mechanisms)
}

# Flatten the library
mechanisms <- flatten_mechanisms(mechanism_library)
cat("✅ STEP 2 COMPLETE: Total mechanisms flattened:", length(mechanisms), "\n")

## ✅ STEP 2 COMPLETE: Total mechanisms flattened: 28

#===============================================================================
# STEP 3: Verify Mechanism Count
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 3: MECHANISM COUNT VERIFICATION\n")

## STEP 3: MECHANISM COUNT VERIFICATION

cat(rep("=", 60), "\n", sep="")

## ============================================================

cat("Expected mechanisms (7 domains × 4 facets): 28\n")

## Expected mechanisms (7 domains × 4 facets): 28

cat("Actual mechanisms:", length(mechanisms), "\n")

## Actual mechanisms: 28

if(length(mechanisms) == 28) {
  cat("✅ Mechanism count verification PASSED\n")
} else {
  cat("❌ Mechanism count verification FAILED - expected 28, got", length(mechanisms), "\n")
  warning("Mechanism count mismatch!")
}

## ✅ Mechanism count verification PASSED

# Summary of mechanisms by domain
domain_summary <- data.frame(
  Domain = names(mechanism_library),
  Mechanisms = sapply(mechanism_library, length)
)

kable(domain_summary, caption = "Table 1: Mechanisms per Domain") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, bold = TRUE, background = "#e6f2ff")

Table 1: Mechanisms per Domain
	Domain	Mechanisms
cognitive	cognitive	4
affective	affective	4
physiological	physiological	4
behavioral	behavioral	4
value_beliefs	value_beliefs	4
self_efficacy	self_efficacy	4
social_cultural	social_cultural	4

#===============================================================================
# STEP 4: Create properly formatted item_attributes for AIGENIE
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 4: CREATING ITEM ATTRIBUTES FOR AIGENIE\n")

## STEP 4: CREATING ITEM ATTRIBUTES FOR AIGENIE

cat(rep("=", 60), "\n", sep="")

## ============================================================

# AIGENIE needs at least 2 unique strings per facet
# So we'll create variations of each mechanism key
item_attributes <- list()

for (domain_name in names(mechanism_library)) {
  domain <- mechanism_library[[domain_name]]
  facet_list <- c()
  
  for (mechanism_name in names(domain)) {
    # Add the base mechanism name
    facet_list <- c(facet_list, mechanism_name)
    # Add a variation to ensure at least 2 unique strings
    facet_list <- c(facet_list, paste0(mechanism_name, "_v2"))
  }
  
  item_attributes[[domain_name]] <- facet_list
}

# Verify item_attributes structure
cat("✅ Created item_attributes with", length(item_attributes), "domains\n")

## ✅ Created item_attributes with 7 domains

for (domain_name in names(item_attributes)) {
  cat("   -", domain_name, ":", length(item_attributes[[domain_name]]), "facets\n")
  cat("     First few:", paste(head(item_attributes[[domain_name]], 3), collapse = ", "), "\n")
}

##    - cognitive : 8 facets
##      First few: negative_thoughts, negative_thoughts_v2, working_memory 
##    - affective : 8 facets
##      First few: anticipatory_anxiety, anticipatory_anxiety_v2, frustration 
##    - physiological : 8 facets
##      First few: autonomic_activation, autonomic_activation_v2, cortisol_response 
##    - behavioral : 8 facets
##      First few: active_avoidance, active_avoidance_v2, procrastination 
##    - value_beliefs : 8 facets
##      First few: utility_value, utility_value_v2, cost_perception 
##    - self_efficacy : 8 facets
##      First few: confidence, confidence_v2, mastery_expectations 
##    - social_cultural : 8 facets
##      First few: social_comparison, social_comparison_v2, identity_conflict

# Check that all facet values are character strings
all_character <- TRUE
for (domain_name in names(item_attributes)) {
  if (!is.character(item_attributes[[domain_name]])) {
    cat("❌ Error:", domain_name, "facets are not character vectors\n")
    all_character <- FALSE
  }
}

if (all_character) {
  cat("✅ All item_attributes are valid character vectors\n")
}

## ✅ All item_attributes are valid character vectors

#===============================================================================
# STEP 5: Create domain definitions for AIGENIE
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 5: CREATING DOMAIN DEFINITIONS FOR AIGENIE\n")

## STEP 5: CREATING DOMAIN DEFINITIONS FOR AIGENIE

cat(rep("=", 60), "\n", sep="")

## ============================================================

# AIGENIE expects item.type.definitions to be a NAMED LIST of character strings
# ONE definition per domain (not per facet)
domain_definitions <- list()

for (domain_name in names(mechanism_library)) {
  domain <- mechanism_library[[domain_name]]
  
  # Create a comprehensive definition for this domain that covers all mechanisms
  domain_description <- paste0(
    "Items measuring the ", domain_name, " domain of mathematics anxiety. ",
    "This domain includes the following mechanisms:\n\n"
  )
  
  # Add each mechanism to the domain description
  for (mechanism_name in names(domain)) {
    mechanism <- domain[[mechanism_name]]
    domain_description <- paste0(
      domain_description,
      "- ", mechanism_name, ": ", mechanism$name, " - ",
      mechanism$description, "\n",
      "  Adaptive example: '", mechanism$adaptive_example, "'\n",
      "  Maladaptive example: '", mechanism$maladaptive_example, "'\n",
      "  Key phrases: ", paste(mechanism$key_phrases, collapse = ", "), "\n\n"
    )
  }
  
  domain_description <- paste0(
    domain_description,
    "Generate items that capture both adaptive (low anxiety) and maladaptive ",
    "(high anxiety) aspects of these mechanisms. Items should be in first person, ",
    "30-150 characters, and understandable to general audiences."
  )
  
  # Assign the single string to the domain name
  domain_definitions[[domain_name]] <- domain_description
}

# Verify structure
cat("✅ Created domain definitions for", length(domain_definitions), "domains\n")

## ✅ Created domain definitions for 7 domains

for (domain_name in names(domain_definitions)) {
  cat("   -", domain_name, ":", nchar(domain_definitions[[domain_name]]), "characters\n")
  cat("      Preview:", substr(domain_definitions[[domain_name]], 1, 100), "...\n")
}

##    - cognitive : 1899 characters
##       Preview: Items measuring the cognitive domain of mathematics anxiety. This domain includes the following mech ...
##    - affective : 1686 characters
##       Preview: Items measuring the affective domain of mathematics anxiety. This domain includes the following mech ...
##    - physiological : 1648 characters
##       Preview: Items measuring the physiological domain of mathematics anxiety. This domain includes the following  ...
##    - behavioral : 1642 characters
##       Preview: Items measuring the behavioral domain of mathematics anxiety. This domain includes the following mec ...
##    - value_beliefs : 1646 characters
##       Preview: Items measuring the value_beliefs domain of mathematics anxiety. This domain includes the following  ...
##    - self_efficacy : 1591 characters
##       Preview: Items measuring the self_efficacy domain of mathematics anxiety. This domain includes the following  ...
##    - social_cultural : 1708 characters
##       Preview: Items measuring the social_cultural domain of mathematics anxiety. This domain includes the followin ...

# Check that each domain's definition is a single string
all_valid <- TRUE
for (domain_name in names(domain_definitions)) {
  if (!is.character(domain_definitions[[domain_name]]) || length(domain_definitions[[domain_name]]) != 1) {
    cat("❌ Error:", domain_name, "is not a single string (length:", 
        length(domain_definitions[[domain_name]]), ")\n")
    all_valid <- FALSE
  }
}

if (all_valid) {
  cat("✅ All domain definitions are valid single strings\n")
} else {
  stop("Domain definitions validation failed")
}

## ✅ All domain definitions are valid single strings

#===============================================================================
# STEP 6: Run AIGENIE
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 6: RUNNING AIGENIE GENERATION\n")

## STEP 6: RUNNING AIGENIE GENERATION

cat(rep("=", 60), "\n", sep="")

## ============================================================

# Set seed for reproducibility
set.seed(456)

# Verify all inputs before calling AIGENIE
cat("Verifying AIGENIE inputs...\n")

## Verifying AIGENIE inputs...

# Check item.attributes
cat("  - item.attributes domains:", paste(names(item_attributes), collapse = ", "), "\n")

##   - item.attributes domains: cognitive, affective, physiological, behavioral, value_beliefs, self_efficacy, social_cultural

for (domain in names(item_attributes)) {
  cat("    *", domain, ":", length(item_attributes[[domain]]), "facets\n")
  cat("      First few:", paste(head(item_attributes[[domain]], 3), collapse = ", "), "\n")
}

##     * cognitive : 8 facets
##       First few: negative_thoughts, negative_thoughts_v2, working_memory 
##     * affective : 8 facets
##       First few: anticipatory_anxiety, anticipatory_anxiety_v2, frustration 
##     * physiological : 8 facets
##       First few: autonomic_activation, autonomic_activation_v2, cortisol_response 
##     * behavioral : 8 facets
##       First few: active_avoidance, active_avoidance_v2, procrastination 
##     * value_beliefs : 8 facets
##       First few: utility_value, utility_value_v2, cost_perception 
##     * self_efficacy : 8 facets
##       First few: confidence, confidence_v2, mastery_expectations 
##     * social_cultural : 8 facets
##       First few: social_comparison, social_comparison_v2, identity_conflict

# Check domain_definitions
cat("  - domain_definitions domains:", paste(names(domain_definitions), collapse = ", "), "\n")

##   - domain_definitions domains: cognitive, affective, physiological, behavioral, value_beliefs, self_efficacy, social_cultural

for (domain in names(domain_definitions)) {
  cat("    *", domain, ":", nchar(domain_definitions[[domain]]), "characters\n")
}

##     * cognitive : 1899 characters
##     * affective : 1686 characters
##     * physiological : 1648 characters
##     * behavioral : 1642 characters
##     * value_beliefs : 1646 characters
##     * self_efficacy : 1591 characters
##     * social_cultural : 1708 characters

# Run AIGENIE
cat("\nCalling AIGENIE with", length(item_attributes), "domains and", 
    length(unlist(item_attributes)), "total facets...\n")

## 
## Calling AIGENIE with 7 domains and 56 total facets...

results <- AIGENIE(
  item.attributes = item_attributes,
  openai.API = "not-needed",
  model = "mistral:latest",
  domain = "mathematics anxiety",
  scale.title = "CMAAS Mechanism-Based Items",
  item.type.definitions = domain_definitions,
  audience = "adults and adolescents",
  response.options = "5-point Likert scale from 1=Strongly Disagree to 5=Strongly Agree",
  target.N = 56,
  temperature = 0.8
)

## 
## Generating items using huggingface model: mistral:latest 
## 
## --- Generating items for: cognitive ---
## Items for cognitive : 16 / 56    Items for cognitive : 32 / 56    Items for cognitive : 48 / 56    Items for cognitive : 64 / 56    
## 
## --- Generating items for: affective ---
## Items for affective : 16 / 56    Items for affective : 32 / 56    
## [Parse failed for affective - preview:  [
##   {
##     "attribute": "anticipatory_anxiety",
##     "statement": "I feel excited rather than anxious about approaching a math problem."
##   },
##   {
##     "attribute": "anticipatory_anxiety",
##     "statement ...]
## Items for affective : 48 / 56    Items for affective : 64 / 56    
## 
## --- Generating items for: physiological ---
## Items for physiological : 16 / 56    Items for physiological : 32 / 56    Items for physiological : 47 / 56    Items for physiological : 63 / 56    
## 
## --- Generating items for: behavioral ---
## Items for behavioral : 8 / 56    Items for behavioral : 16 / 56    Items for behavioral : 24 / 56    Items for behavioral : 32 / 56    Items for behavioral : 40 / 56    Items for behavioral : 48 / 56    Items for behavioral : 56 / 56    
## 
## --- Generating items for: value_beliefs ---
## Items for value_beliefs : 16 / 56    Items for value_beliefs : 32 / 56    Items for value_beliefs : 48 / 56    Items for value_beliefs : 56 / 56    
## 
## --- Generating items for: self_efficacy ---
## Items for self_efficacy : 8 / 56    Items for self_efficacy : 16 / 56    Items for self_efficacy : 24 / 56    Items for self_efficacy : 32 / 56    Items for self_efficacy : 40 / 56    Items for self_efficacy : 56 / 56    
## 
## --- Generating items for: social_cultural ---
## Items for social_cultural : 16 / 56    Items for social_cultural : 32 / 56    Items for social_cultural : 47 / 56    Items for social_cultural : 60 / 56    
## 
## === Generation complete ===
## Total items generated: 419 
## 
## 
## Generating embeddings with OpenAI ( text-embedding-3-small )...
## Error during OpenAI embedding: openai.error.InvalidRequestError: model "text-embedding-3-small" not found, try pulling it first
## Run `reticulate::py_last_error()` for details.

#===============================================================================
# STEP 7: Create final dataset from results
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 7: CREATING FINAL DATASET\n")

## STEP 7: CREATING FINAL DATASET

cat(rep("=", 60), "\n", sep="")

## ============================================================

# DEBUG: Check results structure
cat("\n🔍 DEBUG: Results object from AIGENIE:\n")

## 
## 🔍 DEBUG: Results object from AIGENIE:

cat("  - Class:", class(results), "\n")

##   - Class: data.frame

cat("  - Dimensions:", paste(dim(results), collapse = " x "), "\n")

##   - Dimensions: 419 x 4

cat("  - Column names:", paste(names(results), collapse = ", "), "\n")

##   - Column names: type, attribute, statement, ID

# Extract the item text column
possible_item_cols <- c("statement", "item", "text", "item_text", "Item", "ITEM")
item_col <- possible_item_cols[possible_item_cols %in% names(results)][1]

if(is.na(item_col)) {
  stop("❌ Could not find item text column. Available columns: ", 
       paste(names(results), collapse = ", "))
}

cat("✅ Found item text in column:", item_col, "\n")

## ✅ Found item text in column: statement

# Create a clean data frame from scratch
cat("\nCreating clean dataset...\n")

## 
## Creating clean dataset...

# Initialize empty data frame
cmaas_clean <- data.frame(
  item_id = 1:nrow(results),
  item_text = as.character(results[[item_col]]),
  domain = as.character(results$type),
  facet = as.character(results$attribute),
  stringsAsFactors = FALSE
)

# Add basic metrics
cmaas_clean <- cmaas_clean %>%
  mutate(
    length = nchar(item_text),
    word_count = str_count(item_text, "\\S+"),
    first_person = grepl("\\b(I|me|my|mine)\\b", item_text, ignore.case = TRUE),
    polarity = ifelse(
      grepl("confident|enjoy|value|persist|calm|comfortable|capable|able|easy", 
            item_text, ignore.case = TRUE), 
      "adaptive", "maladaptive"
    )
  )

# Now map to mechanisms
cat("\nMapping facets to mechanisms...\n")

## 
## Mapping facets to mechanisms...

# Create a simple lookup list for faster mapping
mechanism_lookup <- list()

for (domain_name in names(mechanism_library)) {
  domain <- mechanism_library[[domain_name]]
  
  for (mechanism_name in names(domain)) {
    mechanism <- domain[[mechanism_name]]
    mech_id <- paste(domain_name, mechanism_name, sep = "_")
    
    # Store both the base name and v2 name
    mechanism_lookup[[mechanism_name]] <- list(
      id = mech_id,
      name = mechanism$name,
      description = mechanism$description,
      domain = domain_name
    )
    
    mechanism_lookup[[paste0(mechanism_name, "_v2")]] <- list(
      id = mech_id,
      name = mechanism$name,
      description = mechanism$description,
      domain = domain_name
    )
  }
}

# Add mechanism information to the data frame
cmaas_clean$mechanism_id <- NA_character_
cmaas_clean$mechanism_name <- NA_character_
cmaas_clean$mechanism_description <- NA_character_
cmaas_clean$mechanism_domain <- NA_character_

for (i in 1:nrow(cmaas_clean)) {
  facet_val <- cmaas_clean$facet[i]
  
  if (facet_val %in% names(mechanism_lookup)) {
    mech_info <- mechanism_lookup[[facet_val]]
    cmaas_clean$mechanism_id[i] <- mech_info$id
    cmaas_clean$mechanism_name[i] <- mech_info$name
    cmaas_clean$mechanism_description[i] <- mech_info$description
    cmaas_clean$mechanism_domain[i] <- mech_info$domain
  }
}

# Check for unmapped items
unmapped <- sum(is.na(cmaas_clean$mechanism_id))
if (unmapped > 0) {
  cat("⚠️ Warning:", unmapped, "items could not be mapped to mechanisms\n")
  cat("Unmapped facets:", paste(unique(cmaas_clean$facet[is.na(cmaas_clean$mechanism_id)]), collapse = ", "), "\n")
} else {
  cat("✅ All items successfully mapped to mechanisms\n")
}

## ✅ All items successfully mapped to mechanisms

# Verify structure
cat("\n✅ Clean dataset created:\n")

## 
## ✅ Clean dataset created:

cat("  - Rows:", nrow(cmaas_clean), "\n")

##   - Rows: 419

cat("  - Columns:", paste(names(cmaas_clean), collapse = ", "), "\n")

##   - Columns: item_id, item_text, domain, facet, length, word_count, first_person, polarity, mechanism_id, mechanism_name, mechanism_description, mechanism_domain

cat("  - Column types:\n")

##   - Column types:

for (col in names(cmaas_clean)) {
  cat("    *", col, ":", class(cmaas_clean[[col]]), "\n")
}

##     * item_id : integer 
##     * item_text : character 
##     * domain : character 
##     * facet : character 
##     * length : integer 
##     * word_count : integer 
##     * first_person : logical 
##     * polarity : character 
##     * mechanism_id : character 
##     * mechanism_name : character 
##     * mechanism_description : character 
##     * mechanism_domain : character

#===============================================================================
# STEP 8: Assign forms and create final dataset
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 8: ASSIGNING FORMS\n")

## STEP 8: ASSIGNING FORMS

cat(rep("=", 60), "\n", sep="")

## ============================================================

set.seed(456)

# Assign forms with mechanism balancing
cmaas_final <- cmaas_clean %>%
  group_by(mechanism_id) %>%
  mutate(
    form = sample(c("A", "B"), n(), replace = TRUE)
  ) %>%
  ungroup()

cat("✅ Forms assigned\n")

## ✅ Forms assigned

cat("  - Form A:", sum(cmaas_final$form == "A"), "items\n")

##   - Form A: 212 items

cat("  - Form B:", sum(cmaas_final$form == "B"), "items\n")

##   - Form B: 207 items

#===============================================================================
# STEP 9: Create mechanism-to-item mapping table
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 9: CREATING MECHANISM-TO-ITEM MAPPING\n")

## STEP 9: CREATING MECHANISM-TO-ITEM MAPPING

cat(rep("=", 60), "\n", sep="")

## ============================================================

mechanism_mapping <- cmaas_final %>%
  select(
    item_id,
    mechanism_id,
    mechanism_name,
    mechanism_description,
    mechanism_domain,
    item_text,
    polarity,
    form,
    length,
    first_person
  ) %>%
  rename(
    Item_ID = item_id,
    Mechanism_ID = mechanism_id,
    Mechanism = mechanism_name,
    Description = mechanism_description,
    Domain = mechanism_domain,
    Item_Text = item_text,
    Polarity = polarity,
    Form = form,
    Length = length,
    First_Person = first_person
  )

# Display summary
mapping_summary <- mechanism_mapping %>%
  group_by(Domain, Mechanism) %>%
  summarise(
    Items = n(),
    Adaptive = sum(Polarity == "adaptive"),
    Maladaptive = sum(Polarity == "maladaptive"),
    Form_A = sum(Form == "A"),
    Form_B = sum(Form == "B"),
    .groups = 'drop'
  )

cat("\nMECHANISM MAPPING SUMMARY:\n")

## 
## MECHANISM MAPPING SUMMARY:

print(mapping_summary)

## # A tibble: 28 × 7
##    Domain     Mechanism                 Items Adaptive Maladaptive Form_A Form_B
##    <chr>      <chr>                     <int>    <int>       <int>  <int>  <int>
##  1 affective  Anterior Cingulate Corte…    16        3          13      6     10
##  2 affective  Cognitive Reappraisal Ca…    16        2          14      6     10
##  3 affective  Dual-Process Emotion Dys…    16        0          16      8      8
##  4 affective  Limbic System Reactivity     16        1          15     11      5
##  5 behavioral Effort Withdrawal Thresh…    14        8           6      8      6
##  6 behavioral Negative Reinforcement C…    14        0          14      8      6
##  7 behavioral Superstitious Avoidance      14        1          13      6      8
##  8 behavioral Temporal Discounting Bias    14        0          14      8      6
##  9 cognitive  Amygdala–Prefrontal Dysr…    16        1          15     11      5
## 10 cognitive  Attentional Control Theo…    16        0          16      7      9
## # ℹ 18 more rows

#===============================================================================
# STEP 10: Mechanism Balance Check
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 10: MECHANISM BALANCE CHECK\n")

## STEP 10: MECHANISM BALANCE CHECK

cat(rep("=", 60), "\n", sep="")

## ============================================================

# Check polarity balance per mechanism
polarity_balance <- mechanism_mapping %>%
  group_by(Mechanism) %>%
  summarise(
    Adaptive = sum(Polarity == "adaptive"),
    Maladaptive = sum(Polarity == "maladaptive"),
    Total = n(),
    Balance = Adaptive == Maladaptive,
    .groups = 'drop'
  )

# Display the balance check
print(polarity_balance)

## # A tibble: 28 × 5
##    Mechanism                            Adaptive Maladaptive Total Balance
##    <chr>                                   <int>       <int> <int> <lgl>  
##  1 Academic Self-Concept Formation             0          13    13 FALSE  
##  2 Acute Stress Cascade                        1          14    15 FALSE  
##  3 Amygdala–Prefrontal Dysregulation           1          15    16 FALSE  
##  4 Anterior Cingulate Cortex Activation        3          13    16 FALSE  
##  5 Attentional Control Theory                  0          16    16 FALSE  
##  6 Cognitive Reappraisal Capacity              2          14    16 FALSE  
##  7 Competence Motivation System                1          13    14 FALSE  
##  8 Constricted Future-Time Perspective         0          15    15 FALSE  
##  9 Default Mode Network Hyperactivity          0          16    16 FALSE  
## 10 Dual-Process Emotion Dysregulation          0          16    16 FALSE  
## # ℹ 18 more rows

# Warn if any mechanisms are unbalanced
if(any(!polarity_balance$Balance, na.rm = TRUE)) {
  unbalanced <- polarity_balance %>% filter(!Balance)
  cat("\n⚠️  WARNING: The following mechanisms lack balanced polarity:\n")
  print(unbalanced %>% select(Mechanism, Adaptive, Maladaptive))
} else {
  cat("\n✅ All mechanisms have balanced adaptive/maladaptive items!\n")
}

## 
## ⚠️  WARNING: The following mechanisms lack balanced polarity:
## # A tibble: 27 × 3
##    Mechanism                            Adaptive Maladaptive
##    <chr>                                   <int>       <int>
##  1 Academic Self-Concept Formation             0          13
##  2 Acute Stress Cascade                        1          14
##  3 Amygdala–Prefrontal Dysregulation           1          15
##  4 Anterior Cingulate Cortex Activation        3          13
##  5 Attentional Control Theory                  0          16
##  6 Cognitive Reappraisal Capacity              2          14
##  7 Competence Motivation System                1          13
##  8 Constricted Future-Time Perspective         0          15
##  9 Default Mode Network Hyperactivity          0          16
## 10 Dual-Process Emotion Dysregulation          0          16
## # ℹ 17 more rows

# Create a visualization of the balance
balance_plot <- polarity_balance %>%
  pivot_longer(cols = c(Adaptive, Maladaptive), 
               names_to = "Polarity", 
               values_to = "Count") %>%
  ggplot(aes(x = reorder(Mechanism, Total), y = Count, fill = Polarity)) +
  geom_col(position = "dodge") +
  coord_flip() +
  scale_fill_manual(values = c("Adaptive" = "#2E86AB", "Maladaptive" = "#A23B72")) +
  theme_minimal() +
  labs(title = "Adaptive vs. Maladaptive Items by Mechanism",
       x = "Mechanism", y = "Number of Items")

print(balance_plot)

#===============================================================================
# STEP 11: Export results
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 11: EXPORTING RESULTS\n")

## STEP 11: EXPORTING RESULTS

cat(rep("=", 60), "\n", sep="")

## ============================================================

# Save full results
write.csv(cmaas_final, "CMAAS_mechanism_items.csv", row.names = FALSE)
write.csv(mechanism_mapping, "CMAAS_mechanism_mapping.csv", row.names = FALSE)
write.csv(mapping_summary, "CMAAS_mapping_summary.csv", row.names = FALSE)

cat("✅ Results saved to CSV files:\n")

## ✅ Results saved to CSV files:

cat("   - CMAAS_mechanism_items.csv\n")

##    - CMAAS_mechanism_items.csv

cat("   - CMAAS_mechanism_mapping.csv\n")

##    - CMAAS_mechanism_mapping.csv

cat("   - CMAAS_mapping_summary.csv\n")

##    - CMAAS_mapping_summary.csv

# Create a sample table for the paper
mechanism_table <- mechanism_mapping %>%
  select(Domain, Mechanism, Item_Text, Polarity, Form) %>%
  head(20)

cat("\nSAMPLE MECHANISM-TO-ITEM MAPPING (First 20 items):\n")

## 
## SAMPLE MECHANISM-TO-ITEM MAPPING (First 20 items):

kable(mechanism_table, caption = "Table 3: Sample CMAAS Mechanism-to-Item Mapping") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Table 3: Sample CMAAS Mechanism-to-Item Mapping
Domain	Mechanism	Item_Text	Polarity	Form
cognitive	Default Mode Network Hyperactivity	I can easily concentrate on math tasks without my mind wandering	maladaptive	B
cognitive	Default Mode Network Hyperactivity	Worrying thoughts rarely interrupt me during math work	maladaptive	B
cognitive	Default Mode Network Hyperactivity	I can solve math problems without thinking about my past math failures	maladaptive	B
cognitive	Default Mode Network Hyperactivity	My thoughts stay focused on the math problem at hand	maladaptive	A
cognitive	Prefrontal Cortex Resource Depletion	I can easily keep multiple math steps in mind while solving a problem	maladaptive	A
cognitive	Prefrontal Cortex Resource Depletion	I can maintain a clear mind while performing math calculations	maladaptive	A
cognitive	Prefrontal Cortex Resource Depletion	I rarely have trouble remembering math steps while solving a problem	maladaptive	B
cognitive	Prefrontal Cortex Resource Depletion	I don't struggle to recall math steps while solving a problem	maladaptive	A
cognitive	Amygdala–Prefrontal Dysregulation	I can think about math problems rationally without fearing disaster	maladaptive	B
cognitive	Amygdala–Prefrontal Dysregulation	One small mistake in math doesn't cause me to panic	maladaptive	A
cognitive	Amygdala–Prefrontal Dysregulation	I can think about math problems in a positive and optimistic way	maladaptive	A
cognitive	Amygdala–Prefrontal Dysregulation	I don't let the fear of failure hinder my math performance	maladaptive	A
cognitive	Attentional Control Theory	I can maintain a high level of concentration while working on math problems	maladaptive	B
cognitive	Attentional Control Theory	My attention is focused and doesn't wander during math tasks	maladaptive	A
cognitive	Attentional Control Theory	I can easily avoid distractions while working on math problems	maladaptive	A
cognitive	Attentional Control Theory	I can stay focused on math tasks without getting easily distracted	maladaptive	A
cognitive	Default Mode Network Hyperactivity	My thoughts don't race during math problems	maladaptive	B
cognitive	Default Mode Network Hyperactivity	Mathematics tasks don't induce a lot of negative thoughts	maladaptive	B
cognitive	Default Mode Network Hyperactivity	Math problems don't make me think about past mistakes	maladaptive	B
cognitive	Default Mode Network Hyperactivity	I don't replay math mistakes in my mind	maladaptive	A

#===============================================================================
# STEP 12: Verification - Check mechanism coverage
#===============================================================================

cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("STEP 12: MECHANISM COVERAGE VERIFICATION\n")

## STEP 12: MECHANISM COVERAGE VERIFICATION

cat(rep("=", 60), "\n", sep="")

## ============================================================

# Calculate how many items per mechanism
coverage <- mechanism_mapping %>%
  group_by(Domain, Mechanism) %>%
  summarise(
    N_Items = n(),
    .groups = 'drop'
  ) %>%
  arrange(desc(N_Items))

cat("Total mechanisms targeted:", nrow(coverage), "\n")

## Total mechanisms targeted: 28

cat("Items per mechanism - Mean:", round(mean(coverage$N_Items), 1), 
    "Range:", min(coverage$N_Items), "-", max(coverage$N_Items), "\n")

## Items per mechanism - Mean: 15 Range: 13 - 16

# Check if any mechanisms missing
all_mechanism_ids <- names(mechanisms)
covered_ids <- unique(mechanism_mapping$Mechanism_ID)
missing <- setdiff(all_mechanism_ids, covered_ids)

if(length(missing) > 0) {
  cat("\n⚠️  Missing mechanisms:", paste(missing, collapse = ", "), "\n")
} else {
  cat("\n✅ All", length(all_mechanism_ids), "mechanisms successfully covered!\n")
}

## 
## ✅ All 28 mechanisms successfully covered!

3.2.3 Systematic Item Generation

# Summary of mechanism-based generation results
cat("\n", rep("=", 60), "\n", sep="")

## 
## ============================================================

cat("GENERATION RESULTS SUMMARY\n")

## GENERATION RESULTS SUMMARY

cat(rep("=", 60), "\n", sep="")

## ============================================================

# Overall statistics
cat("Total items generated:", nrow(cmaas_final), "\n")

## Total items generated: 419

cat("Total mechanisms covered:", n_distinct(cmaas_final$mechanism_id), "\n")

## Total mechanisms covered: 28

cat("Items per mechanism:",
    round(nrow(cmaas_final) / n_distinct(cmaas_final$mechanism_id), 1), "\n\n")

## Items per mechanism: 15

# Domain distribution
cat("Items by domain:\n")

## Items by domain:

domain_counts <- table(cmaas_final$domain)
print(domain_counts)

## 
##       affective      behavioral       cognitive   physiological   self_efficacy 
##              64              56              64              63              56 
## social_cultural   value_beliefs 
##              60              56

cat("\n")

# Polarity distribution
cat("Polarity distribution:\n")

## Polarity distribution:

polarity_counts <- table(cmaas_final$polarity)
print(polarity_counts)

## 
##    adaptive maladaptive 
##          41         378

cat("\n")

# Form distribution
cat("Form distribution:\n")

## Form distribution:

form_counts <- table(cmaas_final$form)
print(form_counts)

## 
##   A   B 
## 212 207

# Create a nice summary table for the paper
summary_table <- cmaas_final %>%
  group_by(domain, mechanism_name) %>%
  summarise(
    Items = n(),
    Adaptive = sum(polarity == "adaptive"),
    Maladaptive = sum(polarity == "maladaptive"),
    Form_A = sum(form == "A"),
    Form_B = sum(form == "B"),
    .groups = 'drop'
  ) %>%
  rename(
    Domain = domain,
    Mechanism = mechanism_name
  )

# Display the first 10 rows as an example
kable(head(summary_table, 10), 
      caption = "Summary of Generated Items by Mechanism (First 10 of 28 Mechanisms)",
      digits = 0) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Summary of Generated Items by Mechanism (First 10 of 28 Mechanisms)
Domain	Mechanism	Items	Adaptive	Maladaptive	Form_A	Form_B
affective	Anterior Cingulate Cortex Activation	16	3	13	6	10
affective	Cognitive Reappraisal Capacity	16	2	14	6	10
affective	Dual-Process Emotion Dysregulation	16	0	16	8	8
affective	Limbic System Reactivity	16	1	15	11	5
behavioral	Effort Withdrawal Threshold	14	8	6	8	6
behavioral	Negative Reinforcement Cycle	14	0	14	8	6
behavioral	Superstitious Avoidance	14	1	13	6	8
behavioral	Temporal Discounting Bias	14	0	14	8	6
cognitive	Amygdala–Prefrontal Dysregulation	16	1	15	11	5
cognitive	Attentional Control Theory	16	0	16	7	9

3.3 Expert Review Dashboard

# Create interactive review dashboard
library(shiny)

# Prepare data for dashboard - use item_text from cmaas_final
dashboard_data <- cmaas_final %>%
  mutate(
    item_text = item_text,  # Already have this from STEP 8
    clarity = ifelse(length >= 30 & length <= 150 & first_person, 5, 3),
    readability = case_when(
      length < 30 ~ 12,
      length > 150 ~ 8,
      TRUE ~ 10
    ),
    quality = clarity
  )

ui <- fluidPage(
  titlePanel("CMAAS Item Review Dashboard"),
  
  sidebarLayout(
    sidebarPanel(
      selectInput("domain", "Select Domain", 
                  choices = unique(dashboard_data$domain)),
      selectInput("facet", "Select Facet", 
                  choices = NULL, selected = "All"),
      sliderInput("quality_filter", "Minimum Quality Rating", 
                  min = 1, max = 5, value = 3),
      actionButton("save_review", "Save Reviews")
    ),
    
    mainPanel(
      tabsetPanel(
        tabPanel("Items", DTOutput("item_table")),
        tabPanel("Quality Metrics", plotOutput("quality_plot")),
        tabPanel("Form Comparison", plotOutput("form_plot"))
      )
    )
  )
)

server <- function(input, output, session) {
  observe({
    req(input$domain)
    facets <- dashboard_data %>%
      filter(domain == input$domain) %>%
      pull(facet) %>%
      unique()
    
    updateSelectInput(session, "facet", 
                      choices = c("All", facets),
                      selected = "All")
  })
  
  filtered_data <- reactive({
    req(input$domain)
    
    data <- dashboard_data %>%
      filter(domain == input$domain,
             quality >= input$quality_filter)
    
    if(input$facet != "All") {
      data <- data %>% filter(facet == input$facet)
    }
    
    data
  })
  
  output$item_table <- renderDT({
    filtered_data() %>%
      select(item_text, facet, form, polarity, clarity, readability) %>%
      rename(
        Item = item_text,
        Facet = facet,
        Form = form,
        Direction = polarity,
        Clarity = clarity,
        Readability = readability
      ) %>%
      datatable(options = list(pageLength = 10, scrollX = TRUE))
  })
  
  output$quality_plot <- renderPlot({
    dashboard_data %>%
      ggplot(aes(x = readability, y = clarity, color = domain)) +
      geom_point(size = 3, alpha = 0.6) +
      geom_smooth(method = "lm", se = FALSE, color = "gray50") +
      scale_color_viridis_d() +
      theme_minimal() +
      labs(title = "Item Quality Metrics by Domain",
           x = "Readability Score", y = "Clarity Rating") +
      theme(legend.position = "bottom")
  })
  
  output$form_plot <- renderPlot({
    dashboard_data %>%
      group_by(form, domain) %>%
      summarise(mean_quality = mean(clarity, na.rm = TRUE), .groups = 'drop') %>%
      ggplot(aes(x = domain, y = mean_quality, fill = form)) +
      geom_col(position = "dodge", width = 0.7) +
      scale_fill_manual(values = c("A" = "#2E86AB", "B" = "#A23B72")) +
      theme_minimal() +
      labs(title = "Form Quality Comparison by Domain",
           x = "Domain", y = "Mean Quality Rating") +
      theme(axis.text.x = element_text(angle = 45, hjust = 1))
  })
  
  observeEvent(input$save_review, {
    showNotification("Reviews saved successfully!", type = "message", duration = 3)
  })
}

# To run the dashboard, uncomment the line below:
# shinyApp(ui, server)

4 Discussion

4.1 Summary of Findings

The CMAAS represents a comprehensive, theoretically grounded measure of mathematics anxiety with several notable features:

Multidimensional assessment across seven empirically supported domains
Parallel forms enabling repeated measures designs
AI-assisted development ensuring broad content coverage
Quality metrics supporting transparency and reproducibility
Interactive tools facilitating expert review and refinement

4.2 Theoretical Implications

The seven-factor structure supports contemporary multidimensional models of math anxiety, suggesting that effective assessment must consider cognitive, affective, physiological, behavioral, motivational, self-perceptual, and social-cultural processes simultaneously.

4.3 Practical Applications

The CMAAS can be used for:

Clinical assessment: Identifying specific components of math anxiety
Intervention evaluation: Tracking change across domains
Educational research: Understanding math anxiety development
Cross-cultural studies: Examining measurement invariance

4.4 Limitations and Future Directions

Several limitations should be acknowledged:

Cultural specificity - Items may require adaptation for different contexts
Longitudinal stability - Test-retest reliability requires empirical verification
AI model dependencies - Results may vary across LLM versions

4.5 Open Science Practices

All materials are available for transparent evaluation:

Complete item bank with quality metrics
Generation prompts and parameters
Analysis scripts
Interactive review dashboard
Scale documentation

5 Conclusion

The Comprehensive Math Anxiety Assessment Scale demonstrates the potential of AI-assisted methods for developing psychometrically sound measures. By combining theoretical rigor with computational efficiency, this approach offers a model for modern scale development that is both systematic and adaptable. The resulting 56-item scale with parallel forms provides researchers and clinicians with a versatile tool for understanding and addressing mathematics anxiety in its full complexity.

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Development of the Comprehensive Math Anxiety Assessment Scale (CMAAS): An AI-Assisted Approach