# Complete Scientific and Mathematical Inquiry Workflow Demo
#
# This comprehensive example demonstrates the full end-to-end scientific research
# workflow capabilities, from initial problem identification through publication
# and knowledge dissemination.

defmodule CompleteScientificWorkflowDemo do
  @moduledoc """
  Comprehensive demonstration of the complete scientific inquiry workflow.
  
  This demo showcases:
  - End-to-end research pipeline from observation to publication
  - Automated literature review and gap analysis
  - Systematic hypothesis generation and experimental design
  - Adaptive data collection with quality monitoring
  - Comprehensive statistical analysis and interpretation
  - Validation studies and replication protocols
  - Theory building and knowledge integration
  - Automated manuscript generation and dissemination
  """

  # Define a comprehensive research signature for mathematical reasoning
  defmodule MathematicalReasoningResearch do
    use Dspy.Signature
    
    signature_description "Comprehensive mathematical reasoning research with multi-modal analysis"
    signature_instructions "Conduct systematic analysis with rigorous methodology and statistical validation"
    
    input_field :research_question, :string, "Primary research question to investigate"
    input_field :problem_instance, :string, "Specific mathematical problem to analyze"
    input_field :difficulty_level, :string, "Problem complexity (elementary/intermediate/advanced)"
    input_field :domain_context, :string, "Mathematical domain (algebra/geometry/calculus/etc.)"
    input_field :prior_knowledge, :string, "Relevant background knowledge and assumptions"
    
    output_field :solution_approach, :string, "Systematic approach to solving the problem"
    output_field :step_by_step_reasoning, :string, "Detailed reasoning process with justifications"
    output_field :mathematical_proof, :string, "Formal proof or verification when applicable"
    output_field :alternative_methods, :string, "Alternative solution approaches considered"
    output_field :generalization, :string, "How solution generalizes to similar problems"
    output_field :confidence_score, :float, "Confidence in solution correctness (0.0-1.0)"
    output_field :reasoning_depth, :integer, "Number of logical steps in reasoning"
    output_field :conceptual_connections, :string, "Connections to other mathematical concepts"
    output_field :pedagogical_insights, :string, "Educational implications and teaching strategies"
    output_field :research_implications, :string, "Implications for mathematical reasoning research"
  end

  def run_complete_workflow_demo do
    IO.puts """
    🔬 Complete Scientific and Mathematical Inquiry Workflow Demo
    ===========================================================
    
    This demonstration showcases the entire scientific research pipeline
    from initial observations through publication and knowledge dissemination.
    
    Research Focus: Mathematical Reasoning Enhancement in AI Systems
    Timeline: Simulated 6-month research project
    Quality Standards: Maximum rigor with peer review and replication
    """

    # Configure DSPy for research-grade analysis
    Dspy.configure(lm: %Dspy.LM.OpenAI{
      model: "gpt-4.1",
      api_key: System.get_env("OPENAI_API_KEY"),
      timeout: 300_000  # 5 minutes for complex reasoning
    })

    # Run the complete workflow demonstration
    run_end_to_end_research_workflow()
    
    IO.puts "\n✅ Complete scientific workflow demonstration finished!"
  end

  defp run_end_to_end_research_workflow do
    # Stage 1: Initialize Research Project
    IO.puts "\n" <> String.duplicate("=", 80)
    IO.puts "🚀 STAGE 1: RESEARCH PROJECT INITIALIZATION"
    IO.puts String.duplicate("=", 80)
    
    workflow = initialize_research_project()
    display_workflow_overview(workflow)

    # Stage 2: Execute Complete Research Pipeline
    IO.puts "\n" <> String.duplicate("=", 80)
    IO.puts "🔬 STAGE 2: EXECUTING COMPLETE RESEARCH PIPELINE"
    IO.puts String.duplicate("=", 80)
    
    research_results = execute_complete_pipeline(workflow)
    
    # Stage 3: Present Comprehensive Results
    IO.puts "\n" <> String.duplicate("=", 80)
    IO.puts "📊 STAGE 3: COMPREHENSIVE RESEARCH RESULTS"
    IO.puts String.duplicate("=", 80)
    
    present_comprehensive_results(research_results)
    
    # Stage 4: Demonstrate Knowledge Products
    IO.puts "\n" <> String.duplicate("=", 80)
    IO.puts "📚 STAGE 4: KNOWLEDGE PRODUCTS AND DISSEMINATION"
    IO.puts String.duplicate("=", 80)
    
    demonstrate_knowledge_products(research_results)
    
    # Stage 5: Show Impact and Future Directions
    IO.puts "\n" <> String.duplicate("=", 80)
    IO.puts "🎯 STAGE 5: RESEARCH IMPACT AND FUTURE DIRECTIONS"
    IO.puts String.duplicate("=", 80)
    
    demonstrate_impact_and_future_directions(research_results)
  end

  defp initialize_research_project do
    # Define comprehensive research scope
    research_scope = %{
      focus_areas: [
        "mathematical_reasoning_enhancement",
        "step_by_step_problem_solving", 
        "cognitive_load_optimization",
        "error_detection_and_correction",
        "pedagogical_effectiveness"
      ],
      time_horizon: "6_months",
      resource_constraints: %{
        max_experiments: 500,
        budget: 75000,
        computational_budget: 5000,
        human_resources: 4
      },
      success_criteria: %{
        minimum_effect_size: 0.5,
        required_significance_level: 0.01,
        minimum_reproducibility_rate: 0.9,
        minimum_sample_size: 200
      },
      ethical_considerations: [
        "algorithmic_fairness",
        "educational_equity", 
        "transparency_in_ai_reasoning",
        "privacy_protection",
        "responsible_ai_development"
      ]
    }

    # Set maximum quality standards
    quality_standards = %{
      statistical_rigor: :maximum,
      reproducibility: :required,
      peer_review: :double_blind,
      data_sharing: :open,
      preregistration: true
    }

    # Create comprehensive workflow
    Dspy.ScientificInquiryWorkflow.new(
      domain: "mathematical_reasoning_ai",
      research_scope: research_scope,
      quality_standards: quality_standards
    )
  end

  defp display_workflow_overview(workflow) do
    IO.puts "   📋 Research Project Overview:"
    IO.puts "     • Workflow ID: #{workflow.workflow_id}"
    IO.puts "     • Domain: #{workflow.domain}"
    IO.puts "     • Quality Standards: #{workflow.quality_standards.statistical_rigor} rigor"
    IO.puts "     • Timeline: #{workflow.research_scope.time_horizon}"
    IO.puts "     • Budget: $#{workflow.research_scope.resource_constraints.budget}"
    
    IO.puts "   🎯 Research Focus Areas:"
    Enum.each(workflow.research_scope.focus_areas, fn area ->
      IO.puts "     • #{String.replace(area, "_", " ") |> String.capitalize()}"
    end)
    
    IO.puts "   ⚖️ Ethical Considerations:"
    Enum.each(workflow.research_scope.ethical_considerations, fn consideration ->
      IO.puts "     • #{String.replace(consideration, "_", " ") |> String.capitalize()}"
    end)
  end

  defp execute_complete_pipeline(workflow) do
    # Simulate initial observations that trigger research
    initial_observations = [
      "Students consistently struggle with multi-step algebraic problem solving, showing 40% error rates on problems requiring more than 3 steps",
      "Current AI tutoring systems provide answers but fail to teach reasoning strategies, leading to dependency rather than learning",
      "Step-by-step reasoning approaches show promise but lack systematic evaluation across difficulty levels and mathematical domains",
      "Error patterns in mathematical reasoning suggest systematic cognitive biases that could be addressed through targeted interventions",
      "Expert human tutors use specific scaffolding techniques that are not captured in current AI reasoning systems"
    ]

    IO.puts "   🔍 Initial Research Observations:"
    Enum.with_index(initial_observations, 1) |> Enum.each(fn {obs, i} ->
      IO.puts "     #{i}. #{obs}"
    end)

    # Execute the complete research pipeline
    IO.puts "\n   ⚙️ Executing Research Pipeline..."
    IO.puts "     (This is a simulation - real execution would take months)"
    
    case Dspy.ScientificInquiryWorkflow.execute_pipeline(workflow, 
           initial_observations: initial_observations) do
      {:ok, results} ->
        IO.puts "   ✅ Research pipeline completed successfully!"
        results
        
      {:error, reason} ->
        IO.puts "   ❌ Research pipeline failed: #{inspect(reason)}"
        create_mock_results(workflow)
    end
  end

  defp create_mock_results(workflow) do
    # Create comprehensive mock results for demonstration
    %{
      workflow_id: workflow.workflow_id,
      completion_date: DateTime.utc_now(),
      
      scientific_findings: %{
        primary_findings: [
          "Chain-of-thought reasoning with explicit error checking improves mathematical problem-solving accuracy by 34% (p < 0.001, Cohen's d = 0.72)",
          "Cognitive load optimization through structured reasoning reduces solution time by 28% while maintaining accuracy",
          "Error detection mechanisms catch 85% of computational mistakes and 73% of conceptual errors",
          "Pedagogical scaffolding based on expert tutor strategies improves student learning outcomes by 41%",
          "Cross-domain transfer effects observed: algebra skills improved geometry performance by 19%"
        ],
        supporting_evidence: [
          "Meta-analysis of 15 studies with N=3,247 participants across 12 institutions",
          "Randomized controlled trials with pre-registered protocols and independent replication",
          "Effect sizes consistent across age groups (middle school through undergraduate)",
          "Bayesian analysis confirms robust effects with 95% credible intervals excluding null"
        ],
        statistical_significance: %{
          primary_hypothesis: %{p_value: 0.0003, effect_size: 0.72, ci_lower: 0.58, ci_upper: 0.86},
          secondary_hypotheses: [
            %{hypothesis: "cognitive_load_reduction", p_value: 0.0012, effect_size: 0.54},
            %{hypothesis: "error_detection_effectiveness", p_value: 0.0001, effect_size: 0.81},
            %{hypothesis: "pedagogical_improvement", p_value: 0.0004, effect_size: 0.67}
          ]
        },
        effect_sizes: %{
          overall_reasoning_improvement: 0.72,
          accuracy_enhancement: 0.68,
          efficiency_gains: 0.54,
          learning_transfer: 0.43,
          error_reduction: 0.81
        },
        confidence_levels: %{
          primary_findings: 0.99,
          replication_confidence: 0.95,
          generalization_confidence: 0.87,
          practical_significance: 0.92
        },
        limitations: [
          "Limited to mathematical domains - generalization to other subjects unknown",
          "Primarily tested with English-speaking populations",
          "Long-term retention effects require longitudinal follow-up",
          "Implementation complexity may limit real-world adoption"
        ],
        generalizability: %{
          domain_scope: "Mathematical reasoning and related quantitative domains",
          population_scope: "Students aged 12-22, primarily English-speaking",
          context_scope: "Educational settings with digital learning platforms",
          temporal_scope: "Effects stable over 6-month follow-up period"
        }
      },
      
      theoretical_contributions: %{
        new_concepts: [
          "Adaptive Reasoning Scaffolding (ARS): Dynamic adjustment of cognitive support based on real-time performance",
          "Error-Aware Chain-of-Thought (EA-CoT): Reasoning approach with integrated error detection and correction",
          "Cognitive Load Optimization Framework (CLOF): Systematic method for balancing reasoning depth and efficiency",
          "Transfer-Enhanced Learning Architecture (TELA): Design principles for cross-domain skill transfer"
        ],
        theoretical_frameworks: [
          %{
            name: "Integrated Mathematical Reasoning Theory",
            description: "Unified framework combining cognitive load theory, error management, and pedagogical effectiveness",
            components: ["cognitive_architecture", "error_management_system", "scaffolding_mechanisms", "transfer_protocols"],
            validation_status: "empirically_supported"
          }
        ],
        knowledge_integration: %{
          existing_theories_extended: ["Cognitive Load Theory", "Constructivist Learning Theory", "Dual Process Theory"],
          new_theoretical_connections: ["Mathematical cognition ↔ AI reasoning", "Error patterns ↔ Learning strategies"],
          paradigm_contributions: "Bridges human cognitive science and AI reasoning research"
        },
        predictive_models: [
          %{
            name: "Mathematical Reasoning Performance Predictor",
            accuracy: 0.87,
            features: ["problem_complexity", "prior_knowledge", "cognitive_load", "error_patterns"],
            applications: ["adaptive_tutoring", "difficulty_calibration", "intervention_targeting"]
          }
        ]
      },
      
      methodological_contributions: %{
        novel_methods: [
          "Real-time Cognitive Load Assessment via Reasoning Pattern Analysis",
          "Automated Error Classification for Mathematical Problem Solving",
          "Multi-Modal Learning Analytics for Reasoning Research",
          "Adaptive Experimental Design for Educational AI Research"
        ],
        methodological_improvements: [
          "Enhanced statistical power through adaptive sampling",
          "Reduced measurement error via multi-source validation",
          "Improved ecological validity through classroom-based studies",
          "Better control of confounds via matched pair designs"
        ],
        best_practices: [
          "Pre-register all hypotheses and analysis plans",
          "Use multiple complementary assessment methods",
          "Include both immediate and delayed outcome measures",
          "Ensure diverse participant populations",
          "Replicate findings across multiple sites",
          "Share all data and analysis code openly"
        ],
        quality_standards: %{
          reproducibility_rate: 0.94,
          inter_rater_reliability: 0.91,
          test_retest_reliability: 0.88,
          construct_validity: 0.85,
          external_validity: 0.79
        }
      },
      
      practical_applications: %{
        immediate_applications: [
          "Enhanced AI tutoring systems with error-aware reasoning",
          "Adaptive problem difficulty calibration in learning platforms",
          "Teacher training programs on mathematical reasoning strategies",
          "Diagnostic tools for identifying student reasoning difficulties"
        ],
        technology_transfer: %{
          commercial_potential: "High - applicable to $12B educational technology market",
          implementation_readiness: "Medium - requires 12-18 months development",
          scalability: "High - cloud-based deployment possible",
          cost_effectiveness: "Positive ROI projected within 24 months"
        },
        policy_implications: [
          "Update mathematics curriculum standards to include explicit reasoning instruction",
          "Establish quality standards for AI-based educational tools",
          "Develop teacher certification requirements for AI-assisted instruction",
          "Create research funding priorities for human-AI collaborative learning"
        ],
        industry_relevance: %{
          education_technology: "Direct application in adaptive learning platforms",
          assessment_industry: "Enhanced diagnostic and formative assessment tools",
          corporate_training: "Applicable to quantitative reasoning training programs",
          government_education: "Informs evidence-based policy development"
        }
      },
      
      generated_publications: [
        create_mock_primary_publication(),
        create_mock_methodological_publication(),
        create_mock_theoretical_publication(),
        create_mock_practical_publication()
      ],
      
      knowledge_base_updates: %{
        concept_additions: 47,
        relationship_updates: 128,
        theory_integrations: 8,
        citation_network_updates: 234
      },
      
      recommended_next_steps: %{
        immediate_follow_ups: [
          "Longitudinal study of 18-month learning outcomes",
          "Cross-cultural validation with non-English speaking populations", 
          "Implementation study in real classroom settings",
          "Extension to other STEM domains (physics, chemistry)"
        ],
        long_term_research_directions: [
          "Neural mechanisms underlying mathematical reasoning improvements",
          "Personalization algorithms for individual learning differences",
          "Integration with embodied and multimodal learning approaches",
          "Societal impacts of AI-enhanced mathematical education"
        ],
        collaboration_opportunities: [
          "Partnership with major educational technology companies",
          "International consortium for cross-cultural validation",
          "Collaboration with cognitive neuroscience research groups",
          "Joint projects with mathematics education researchers"
        ],
        funding_opportunities: [
          "NSF Education and Human Resources Core Research ($2.5M, 5 years)",
          "NIH Science of Learning and Development ($1.8M, 4 years)",
          "Department of Education Education Innovation Research ($3.2M, 5 years)",
          "Private foundation grants for educational equity ($1.2M, 3 years)"
        ]
      },
      
      quality_assessment: %{
        overall_quality_score: 0.92,
        stage_quality_scores: %{
          problem_identification: 0.89,
          literature_review: 0.94,
          hypothesis_generation: 0.91,
          experimental_design: 0.96,
          data_collection: 0.90,
          analysis: 0.95,
          validation: 0.93,
          theory_building: 0.88,
          communication: 0.90
        },
        reproducibility_score: 0.94,
        impact_prediction: 0.83
      },
      
      resource_utilization: %{
        time_investment: 187, # days
        computational_resources: %{
          cpu_hours: 2847,
          gpu_hours: 456,
          storage_gb: 2300,
          api_calls: 45670
        },
        financial_costs: 68400,
        efficiency_metrics: %{
          cost_per_finding: 13680,
          time_per_experiment: 0.37,
          success_rate: 0.89
        }
      },
      
      workflow_metadata: %{
        total_duration: 187,
        stage_durations: %{
          problem_identification: 12,
          literature_review: 18,
          hypothesis_generation: 14,
          experimental_design: 21,
          data_collection: 78,
          analysis: 19,
          validation: 15,
          theory_building: 7,
          communication: 3
        },
        decision_points: 23,
        adaptations_made: 8,
        quality_checkpoints: 15
      }
    }
  end

  defp present_comprehensive_results(results) do
    IO.puts "   📊 Primary Scientific Findings:"
    Enum.with_index(results.scientific_findings.primary_findings, 1) |> Enum.each(fn {finding, i} ->
      IO.puts "     #{i}. #{finding}"
    end)

    IO.puts "\n   📈 Statistical Significance:"
    primary = results.scientific_findings.statistical_significance.primary_hypothesis
    IO.puts "     • Primary Hypothesis: p = #{primary.p_value}, d = #{primary.effect_size}"
    IO.puts "     • 95% CI: [#{primary.ci_lower}, #{primary.ci_upper}]"
    
    IO.puts "\n   🎯 Effect Sizes:"
    Enum.each(results.scientific_findings.effect_sizes, fn {metric, size} ->
      magnitude = cond do
        size >= 0.8 -> "Large"
        size >= 0.5 -> "Medium"
        size >= 0.2 -> "Small"
        true -> "Negligible"
      end
      IO.puts "     • #{String.replace(to_string(metric), "_", " ") |> String.capitalize()}: #{size} (#{magnitude})"
    end)

    IO.puts "\n   🔍 Quality Assessment:"
    IO.puts "     • Overall Quality Score: #{results.quality_assessment.overall_quality_score}"
    IO.puts "     • Reproducibility Score: #{results.quality_assessment.reproducibility_score}"
    IO.puts "     • Predicted Impact Score: #{results.quality_assessment.impact_prediction}"

    IO.puts "\n   ⚠️ Key Limitations:"
    Enum.each(results.scientific_findings.limitations, fn limitation ->
      IO.puts "     • #{limitation}"
    end)
  end

  defp demonstrate_knowledge_products(results) do
    IO.puts "   📚 Generated Publications:"
    Enum.with_index(results.generated_publications, 1) |> Enum.each(fn {pub, i} ->
      IO.puts "     #{i}. #{pub.title}"
      IO.puts "        Journal: #{pub.target_journal} (Impact Factor: #{pub.estimated_impact_factor})"
      IO.puts "        Status: #{pub.submission_status}"
    end)

    IO.puts "\n   🧠 Theoretical Contributions:"
    Enum.with_index(results.theoretical_contributions.new_concepts, 1) |> Enum.each(fn {concept, i} ->
      IO.puts "     #{i}. #{concept}"
    end)

    IO.puts "\n   🔧 Methodological Innovations:"
    Enum.with_index(results.methodological_contributions.novel_methods, 1) |> Enum.each(fn {method, i} ->
      IO.puts "     #{i}. #{method}"
    end)

    IO.puts "\n   🌐 Knowledge Base Integration:"
    kb = results.knowledge_base_updates
    IO.puts "     • New Concepts Added: #{kb.concept_additions}"
    IO.puts "     • Relationships Updated: #{kb.relationship_updates}"
    IO.puts "     • Theory Integrations: #{kb.theory_integrations}"
    IO.puts "     • Citation Network Updates: #{kb.citation_network_updates}"

    # Demonstrate automated manuscript generation
    demonstrate_manuscript_generation(results)
  end

  defp demonstrate_manuscript_generation(results) do
    IO.puts "\n   📝 Automated Manuscript Generation Sample:"
    
    manuscript_excerpt = """
    
    ABSTRACT
    
    Background: Mathematical reasoning remains a significant challenge in AI systems,
    with current approaches showing limitations in step-by-step problem solving and
    error detection. This study investigated the effectiveness of enhanced reasoning
    frameworks in improving mathematical problem-solving performance.
    
    Methods: We conducted a randomized controlled trial with #{extract_sample_size(results)} 
    participants across #{extract_site_count(results)} research sites. Participants were 
    randomly assigned to enhanced reasoning (n=#{div(extract_sample_size(results), 2)}) or 
    control conditions (n=#{div(extract_sample_size(results), 2)}). Primary outcomes included 
    problem-solving accuracy, reasoning efficiency, and error rates.
    
    Results: Enhanced reasoning demonstrated significant improvements in accuracy 
    (d = #{results.scientific_findings.effect_sizes.overall_reasoning_improvement}, 
    p < 0.001), with #{round(results.scientific_findings.effect_sizes.overall_reasoning_improvement * 34)}% 
    improvement over baseline. Error detection effectiveness reached 85% for computational 
    errors and 73% for conceptual errors. Cross-domain transfer effects were observed 
    with 19% improvement in related mathematical areas.
    
    Conclusions: These findings provide strong evidence for the effectiveness of 
    enhanced reasoning approaches in mathematical problem solving, with implications 
    for both AI system design and educational technology development.
    
    Keywords: mathematical reasoning, artificial intelligence, educational technology,
    cognitive load, error detection
    """
    
    IO.puts manuscript_excerpt
    
    IO.puts "\n   📊 Generated Tables and Figures:"
    IO.puts "     • Table 1: Participant Demographics and Baseline Characteristics"
    IO.puts "     • Table 2: Primary and Secondary Outcome Measures"
    IO.puts "     • Table 3: Effect Sizes and Confidence Intervals by Subgroup"
    IO.puts "     • Figure 1: Flow Diagram of Participant Recruitment and Retention"
    IO.puts "     • Figure 2: Primary Outcome Results with Error Bars"
    IO.puts "     • Figure 3: Cognitive Load Analysis Across Conditions"
    IO.puts "     • Figure 4: Error Pattern Classification and Frequency"
  end

  defp demonstrate_impact_and_future_directions(results) do
    IO.puts "   🎯 Immediate Practical Applications:"
    Enum.with_index(results.practical_applications.immediate_applications, 1) |> Enum.each(fn {app, i} ->
      IO.puts "     #{i}. #{app}"
    end)

    IO.puts "\n   💼 Technology Transfer Potential:"
    transfer = results.practical_applications.technology_transfer
    IO.puts "     • Commercial Potential: #{transfer.commercial_potential}"
    IO.puts "     • Implementation Timeline: #{transfer.implementation_readiness}"
    IO.puts "     • Scalability: #{transfer.scalability}"
    IO.puts "     • Economic Impact: #{transfer.cost_effectiveness}"

    IO.puts "\n   🔮 Future Research Directions:"
    Enum.with_index(results.recommended_next_steps.long_term_research_directions, 1) |> Enum.each(fn {direction, i} ->
      IO.puts "     #{i}. #{direction}"
    end)

    IO.puts "\n   🤝 Collaboration Opportunities:"
    Enum.with_index(results.recommended_next_steps.collaboration_opportunities, 1) |> Enum.each(fn {collab, i} ->
      IO.puts "     #{i}. #{collab}"
    end)

    IO.puts "\n   💰 Funding Opportunities:"
    Enum.with_index(results.recommended_next_steps.funding_opportunities, 1) |> Enum.each(fn {funding, i} ->
      IO.puts "     #{i}. #{funding}"
    end)

    # Resource utilization summary
    demonstrate_resource_efficiency(results)
    
    # Timeline and workflow insights
    demonstrate_workflow_insights(results)
  end

  defp demonstrate_resource_efficiency(results) do
    IO.puts "\n   💡 Resource Utilization Efficiency:"
    resources = results.resource_utilization
    IO.puts "     • Total Project Duration: #{resources.time_investment} days"
    IO.puts "     • Financial Investment: $#{resources.financial_costs}"
    IO.puts "     • Cost per Major Finding: $#{resources.efficiency_metrics.cost_per_finding}"
    IO.puts "     • Research Success Rate: #{round(resources.efficiency_metrics.success_rate * 100)}%"
    IO.puts "     • Computational Resources: #{resources.computational_resources.cpu_hours} CPU hours"
    
    efficiency_rating = cond do
      resources.efficiency_metrics.success_rate >= 0.85 -> "Excellent"
      resources.efficiency_metrics.success_rate >= 0.75 -> "Good"  
      resources.efficiency_metrics.success_rate >= 0.65 -> "Acceptable"
      true -> "Needs Improvement"
    end
    
    IO.puts "     • Overall Efficiency Rating: #{efficiency_rating}"
  end

  defp demonstrate_workflow_insights(results) do
    IO.puts "\n   🔄 Workflow Process Insights:"
    metadata = results.workflow_metadata
    IO.puts "     • Total Process Duration: #{metadata.total_duration} days"
    IO.puts "     • Number of Decision Points: #{metadata.decision_points}"
    IO.puts "     • Adaptive Adjustments Made: #{metadata.adaptations_made}"
    IO.puts "     • Quality Checkpoints: #{metadata.quality_checkpoints}"
    
    IO.puts "\n   ⏱️ Stage Duration Breakdown:"
    Enum.each(metadata.stage_durations, fn {stage, duration} ->
      percentage = round(duration / metadata.total_duration * 100)
      stage_name = String.replace(to_string(stage), "_", " ") |> String.capitalize()
      IO.puts "     • #{stage_name}: #{duration} days (#{percentage}%)"
    end)
    
    # Identify most time-intensive stages
    longest_stages = metadata.stage_durations
    |> Enum.sort_by(fn {_stage, duration} -> duration end, :desc)
    |> Enum.take(3)
    
    IO.puts "\n   📊 Most Time-Intensive Stages:"
    Enum.with_index(longest_stages, 1) |> Enum.each(fn {{stage, duration}, i} ->
      stage_name = String.replace(to_string(stage), "_", " ") |> String.capitalize()
      IO.puts "     #{i}. #{stage_name}: #{duration} days"
    end)
  end

  # Helper functions for mock data generation

  defp create_mock_primary_publication do
    %{
      title: "Enhanced Mathematical Reasoning in AI Systems: A Randomized Controlled Trial of Error-Aware Chain-of-Thought Approaches",
      authors: ["Dr. Research Lead", "Dr. Statistical Expert", "Dr. Domain Specialist", "Dr. Educational Technologist"],
      target_journal: "Journal of Artificial Intelligence Research",
      estimated_impact_factor: 4.8,
      submission_status: "under_review",
      word_count: 8500,
      figures: 6,
      tables: 4,
      references: 87,
      abstract: "This study presents the first large-scale randomized controlled trial...",
      keywords: ["mathematical reasoning", "chain-of-thought", "error detection", "educational AI"],
      submission_date: DateTime.add(DateTime.utc_now(), -30, :day),
      estimated_publication_date: DateTime.add(DateTime.utc_now(), 120, :day)
    }
  end

  defp create_mock_methodological_publication do
    %{
      title: "Real-Time Cognitive Load Assessment for Mathematical Reasoning Research: Methodology and Validation",
      authors: ["Dr. Methodological Expert", "Dr. Cognitive Scientist", "Dr. Research Lead"],
      target_journal: "Behavior Research Methods", 
      estimated_impact_factor: 3.2,
      submission_status: "revision_requested",
      word_count: 6800,
      figures: 4,
      tables: 3,
      references: 65,
      abstract: "We present a novel methodology for real-time assessment...",
      keywords: ["cognitive load", "real-time assessment", "mathematical reasoning", "methodology"],
      submission_date: DateTime.add(DateTime.utc_now(), -45, :day),
      estimated_publication_date: DateTime.add(DateTime.utc_now(), 90, :day)
    }
  end

  defp create_mock_theoretical_publication do
    %{
      title: "Integrated Mathematical Reasoning Theory: Bridging Cognitive Science and Artificial Intelligence",
      authors: ["Dr. Theoretical Expert", "Dr. Research Lead", "Dr. Cognitive Scientist"],
      target_journal: "Psychological Review",
      estimated_impact_factor: 7.6,
      submission_status: "in_preparation",
      word_count: 12000,
      figures: 8,
      tables: 2,
      references: 156,
      abstract: "We propose a unified theoretical framework that integrates...",
      keywords: ["theoretical framework", "mathematical cognition", "AI reasoning", "cognitive architecture"],
      submission_date: DateTime.add(DateTime.utc_now(), 15, :day),
      estimated_publication_date: DateTime.add(DateTime.utc_now(), 180, :day)
    }
  end

  defp create_mock_practical_publication do
    %{
      title: "From Research to Practice: Implementing AI-Enhanced Mathematical Reasoning in Educational Settings",
      authors: ["Dr. Educational Technologist", "Dr. Implementation Specialist", "Dr. Research Lead"],
      target_journal: "Computers & Education",
      estimated_impact_factor: 5.4,
      submission_status: "planning",
      word_count: 7200,
      figures: 5,
      tables: 3,
      references: 72,
      abstract: "This paper describes the translation of research findings...",
      keywords: ["educational technology", "implementation", "mathematical reasoning", "classroom practice"],
      submission_date: DateTime.add(DateTime.utc_now(), 45, :day),
      estimated_publication_date: DateTime.add(DateTime.utc_now(), 210, :day)
    }
  end

  defp extract_sample_size(_results), do: 1624
  defp extract_site_count(_results), do: 12

  # Additional helper functions for demo
  defp round(number) when is_float(number), do: Float.round(number, 2)
  defp round(number), do: number

  def run_specialized_workflow_demos do
    IO.puts """
    🎛️ Specialized Workflow Demonstrations
    ====================================
    
    Additional demonstrations of specialized research workflows:
    """

    demonstrate_meta_analysis_workflow()
    demonstrate_replication_study_workflow()
    demonstrate_theory_development_workflow()
    demonstrate_cross_cultural_validation_workflow()
  end

  defp demonstrate_meta_analysis_workflow do
    IO.puts "\n📊 Meta-Analysis Workflow:"
    IO.puts "   • Systematic literature search across 12 databases"
    IO.puts "   • Quality assessment using standardized criteria"
    IO.puts "   • Random-effects meta-analysis with subgroup analyses"
    IO.puts "   • Publication bias assessment and correction"
    IO.puts "   • Heterogeneity analysis and moderator testing"
    IO.puts "   • GRADE evidence quality assessment"
  end

  defp demonstrate_replication_study_workflow do
    IO.puts "\n🔄 Replication Study Workflow:"
    IO.puts "   • Direct replication with original materials and procedures"
    IO.puts "   • Conceptual replication with modified contexts"
    IO.puts "   • Multi-site coordinated replication"
    IO.puts "   • Pre-registered protocols with analysis plans"
    IO.puts "   • Statistical equivalence testing"
    IO.puts "   • Replication success criteria evaluation"
  end

  defp demonstrate_theory_development_workflow do
    IO.puts "\n🧠 Theory Development Workflow:"
    IO.puts "   • Systematic concept extraction from empirical findings"
    IO.puts "   • Formal theory specification with mathematical models"
    IO.puts "   • Computational theory implementation and testing"
    IO.puts "   • Predictive validity assessment"
    IO.puts "   • Theory comparison and selection"
    IO.puts "   • Integration with existing theoretical frameworks"
  end

  defp demonstrate_cross_cultural_validation_workflow do
    IO.puts "\n🌍 Cross-Cultural Validation Workflow:"
    IO.puts "   • Cultural adaptation of instruments and procedures"
    IO.puts "   • Multi-country coordinated data collection"
    IO.puts "   • Measurement invariance testing"
    IO.puts "   • Cultural moderator analysis"
    IO.puts "   • Cross-cultural generalizability assessment"
    IO.puts "   • Culturally-sensitive interpretation guidelines"
  end
end

# Execute the demonstration
if __ENV__.file == Path.absname(__ENV__.file) do
  CompleteScientificWorkflowDemo.run_complete_workflow_demo()
  
  IO.puts "\n" <> String.duplicate("=", 80)
  IO.puts "🎯 SPECIALIZED WORKFLOWS"
  IO.puts String.duplicate("=", 80)
  
  CompleteScientificWorkflowDemo.run_specialized_workflow_demos()
  
  IO.puts """
  
  🎉 Complete Scientific and Mathematical Inquiry Workflow Demo Finished!
  =====================================================================
  
  This comprehensive demonstration showcased:
  
  🔬 COMPLETE RESEARCH PIPELINE:
  ✅ Problem identification and literature review
  ✅ Systematic hypothesis generation and testing
  ✅ Rigorous experimental design and execution
  ✅ Comprehensive statistical analysis and interpretation
  ✅ Validation studies and replication protocols
  ✅ Theory building and knowledge integration
  ✅ Automated manuscript generation and dissemination
  
  📊 QUALITY ASSURANCE FEATURES:
  ✅ Maximum statistical rigor with pre-registration
  ✅ Independent replication and validation
  ✅ Open data and reproducible analysis
  ✅ Peer review coordination and response
  ✅ Comprehensive quality metrics and tracking
  
  🎯 PRACTICAL APPLICATIONS:
  ✅ Technology transfer and commercialization pathways
  ✅ Policy implications and recommendations
  ✅ Educational implementation strategies
  ✅ Future research direction identification
  
  🤝 COLLABORATION FEATURES:
  ✅ Multi-site coordination and data sharing
  ✅ Expert network integration and consultation
  ✅ Stakeholder engagement and communication
  ✅ Impact tracking and dissemination optimization
  
  This framework provides researchers with a complete end-to-end system
  for conducting rigorous, reproducible, and impactful scientific research
  in mathematical reasoning and related domains.
  
  The system integrates:
  • Advanced AI reasoning capabilities
  • Rigorous statistical methodology
  • Comprehensive quality assurance
  • Automated knowledge management
  • Collaborative research workflows
  • Professional publication preparation
  
  Ready for deployment in academic, industry, and government research settings.
  """
end