{"id":109375,"date":"2025-07-08T18:41:30","date_gmt":"2025-07-08T13:11:30","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/?page_id=109375"},"modified":"2025-07-02T16:30:53","modified_gmt":"2025-07-02T11:00:53","slug":"generative-ai-quiz","status":"publish","type":"page","link":"https:\/\/www.mygreatlearning.com\/blog\/generative-ai-quiz\/","title":{"rendered":"Generative AI Quiz"},"content":{"rendered":"\n<div id=\"gen-ai-quiz-app-wrapper\">\n    <style>\n        \/* --- General Reset & Variables --- *\/\n        html { scroll-behavior: smooth; }\n        #gen-ai-quiz-app-wrapper {\n            --primary-blue: #1A73E8; --hover-blue: #1865c9; --background-grey: #F8F9FA; --border-grey: #DADCE0; --text-primary: #202124; --text-secondary: #5F6368; --option-bg: #FFFFFF; --option-hover-bg: #F1F3F4; --color-success: #1E8E3E; --correct-bg: #E6F4EA; --correct-text: #117233; --color-danger: #D93025; --incorrect-bg: #FCE8E6; --incorrect-text: #A50E0E; --color-warning: #f59e0b; --whatsapp-green: #25D366; --twitter-blue: #1DA1F2; --linkedin-blue: #0A66C2; --telegram-blue: #24A1DE; --shadow: 0 1px 2px 0 rgba(60, 64, 67, 0.3), 0 2px 6px 2px rgba(60, 64, 67, 0.15); --shadow-lg: 0 10px 25px rgba(0, 0, 0, 0.1); font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, sans-serif; color: var(--text-primary); box-sizing: border-box;\n        }\n        #gen-ai-quiz-app-wrapper *, #gen-ai-quiz-app-wrapper *::before, #gen-ai-quiz-app-wrapper *::after { box-sizing: inherit; }\n        #quiz-app-container { width: 1200px; max-width: 100%; border-radius: 8px; overflow: hidden; margin: 50px auto auto auto; }\n        #gen-ai-quiz-app-wrapper .quiz-screen { display: none; }\n        #gen-ai-quiz-app-wrapper .quiz-screen.active { display: block; animation: fadeIn 0.5s ease-out; }\n        @keyframes fadeIn { from { opacity: 0; transform: translateY(15px); } to { opacity: 1; transform: translateY(0); } }\n        \n        \/* --- Screen Padding & Header --- *\/\n        #main-menu-view, #user-details-view, #analysis-view { padding: 2rem 3rem; }\n        #gen-ai-quiz-app-wrapper .quiz-header { text-align: center; margin-bottom: 2.5rem; }\n        #gen-ai-quiz-app-wrapper .quiz-header h1 { font-size: 2.5rem; margin: 0 0 0.5rem 0; }\n        #gen-ai-quiz-app-wrapper .quiz-header p { font-size: 1.1rem; color: var(--text-secondary); max-width: 600px; margin: 0 auto; }\n        \n        \/* --- Main Menu --- *\/\n        #gen-ai-quiz-app-wrapper .difficulty-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1.5rem; margin-bottom: 3rem; }\n        #gen-ai-quiz-app-wrapper .difficulty-card { border: 1px solid var(--border-grey); background-color: #fff; border-radius: 8px; text-align: center; padding: 1.5rem; cursor: pointer; transition: transform 0.2s ease, box-shadow 0.2s ease; display: flex; flex-direction: column; }\n        #gen-ai-quiz-app-wrapper .difficulty-card:hover { transform: translateY(-5px); box-shadow: var(--shadow-lg); }\n        #gen-ai-quiz-app-wrapper .difficulty-card .icon { font-size: 2.5rem; margin-bottom: 1rem; }\n        #gen-ai-quiz-app-wrapper .difficulty-card h2 { font-size: 1.5rem; margin: 0 0 0.5rem 0; }\n        #gen-ai-quiz-app-wrapper .difficulty-card p { font-size: 0.9rem; color: var(--text-secondary); margin: 0 0 1.5rem 0; }\n        #gen-ai-quiz-app-wrapper .difficulty-card .features { font-size: 0.875rem; color: var(--text-secondary); text-align: left; margin-bottom: 1.5rem; flex-grow: 1; }\n        #gen-ai-quiz-app-wrapper .difficulty-card .features p { margin: 0.5rem 0; }\n        #gen-ai-quiz-app-wrapper .start-btn { width: 100%; }\n        #gen-ai-quiz-app-wrapper .difficulty-card.beginner h2 { color: var(--color-success); }\n        #gen-ai-quiz-app-wrapper .difficulty-card.intermediate h2 { color: var(--color-warning); }\n        #gen-ai-quiz-app-wrapper .difficulty-card.advanced h2 { color: var(--color-danger); }\n        #gen-ai-quiz-app-wrapper .quiz-features-section { text-align: center; background-color: #fff; padding: 2rem; border-radius: 8px; border: 1px solid var(--border-grey); 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cursor: not-allowed; }\n        \n        \/* --- Quiz Header & Progress --- *\/\n        #quiz-view { background-color: #fff; }\n        #quiz-header, #results-header, #analysis-header { display: flex; justify-content: space-between; align-items: center; padding: 1rem 1.5rem; border-bottom: 1px solid var(--border-grey); }\n        #quiz-header h1, #results-header h1, #analysis-header h1 { font-size: 1.25rem; margin: 0; }\n        #gen-ai-quiz-app-wrapper .header-actions { display: flex; align-items: center; gap: 1rem; }\n        #progress-stepper { display: flex; gap: 0.5rem; flex-grow: 1; margin: 0 2rem; }\n        #gen-ai-quiz-app-wrapper .step { height: 4px; flex-grow: 1; background-color: #E0E0E0; border-radius: 2px; transition: background-color 0.3s ease; }\n        #gen-ai-quiz-app-wrapper .step.active { background-color: var(--primary-blue); }\n        #gen-ai-quiz-app-wrapper .step.correct { background-color: var(--color-success); }\n        #gen-ai-quiz-app-wrapper .step.incorrect { background-color: var(--color-danger); 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}\n        #gen-ai-quiz-app-wrapper .progress-bar-fg { height: 100%; background: var(--primary-blue); border-radius: 4px; transition: width 0.5s ease-out; }\n        #gen-ai-quiz-app-wrapper .question-breakdown { font-size: 0.875rem; margin-top: 1rem; text-align: left; }\n        #gen-ai-quiz-app-wrapper .question-breakdown strong { color: var(--correct-text); }\n        #gen-ai-quiz-app-wrapper .question-breakdown span { color: var(--incorrect-text); }\n        #gen-ai-quiz-app-wrapper .hidden { display: none; }\n\n        \/* --- START: New Results View Styles --- *\/\n        #results-view .container { max-width: 1100px; margin: 0 auto; padding: 2rem 1rem; color: #1A202C;}\n        #results-view .page-header { display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 2rem; flex-wrap: wrap; }\n        #results-view .header-title h1 { font-size: 2rem; font-weight: 700; margin: 0; }\n        #results-view .header-title p { color: #4A5568; margin-top: 0.25rem; }\n        #results-view .restart-quiz-btn { display: flex; align-items: center; gap: 0.5rem; padding: 0.6rem 1.2rem; background-color: #4353FF; border: 1px solid #E2E8F0; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; color: white; cursor: pointer; transition: background-color 0.2s; margin-top: 1rem; }\n        #results-view .restart-quiz-btn:hover { background-color: #4353FF; }\n        #results-view .main-content { display: grid; grid-template-columns: 1fr; gap: 2rem; }\n        #results-view .card { background-color: #ffffff; padding: 1.5rem; }\n        #results-view .score-card { text-align: center; color:gray;}\n        #results-view .score-card h2 { font-size: 1.125rem; font-weight: 600; margin-top: 0; margin-bottom: 1rem; }\n        #results-view .score-visual { position: relative; width: 10rem; height: 10rem; margin: 0 auto; }\n        #results-view .score-visual svg { width: 100%; height: 100%; }\n        #results-view .progress-ring-circle { transition: stroke-dashoffset 0.8s ease-out; transform: rotate(-90deg); transform-origin: 50% 50%; }\n        #results-view .score-text { position: absolute; top: 0; left: 0; right: 0; bottom: 0; display: flex; flex-direction: column; justify-content: center; align-items: center; }\n        #results-view .score-text .score-value { font-size: 2.25rem; font-weight: 700; }\n        #results-view .score-text .score-value span { font-size: 1.5rem; color: #4A5568; }\n        #results-view .score-text .score-label { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; }\n        #results-view .score-breakdown { margin-top: 1.5rem; display: grid; grid-template-columns: repeat(3, 1fr); gap: 1rem; }\n        #results-view .score-breakdown div span { font-weight: 700; font-size: 1.125rem; }\n        #results-view .score-breakdown div p { font-size: 0.75rem; color: #4A5568; margin: 0; }\n        #results-view .text-correct { color: #22c55e; }\n        #results-view .text-incorrect { color: #ef4444; }\n        #results-view .text-skipped { color: #6b7280; }\n        #results-view .analysis-card { display: flex; align-items: flex-start; gap: 1rem; justify-content: center; color:gray; }\n        #results-view .analysis-icon { flex-shrink: 0; width: 2.5rem; height: 2.5rem; background-color: #F0F1FF; border-radius: 0.5rem; display: flex; align-items: center; justify-content: center; }\n        #results-view .analysis-content h3 { font-weight: 600; margin: 0; font-size: 1.2rem; }\n        #results-view .analysis-content p { font-size: 0.875rem; color: #4A5568; margin: 0.25rem 0 1rem 0; }\n        #results-view .primary-button { width: 100%; padding: 0.6rem 1.2rem; background-color: #4353FF; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; color: #ffffff; border: none; cursor: pointer; transition: background-color 0.2s; }\n        #results-view .primary-button:hover { background-color: #2539cc; }\n        #results-view .learning-journey-card h2 { font-size: 1.5rem; font-weight: 700; margin-top: 0; }\n        #results-view .learning-journey-card > p { color: #4A5568; margin: 0.25rem 0 2rem 0; }\n        #results-view .course-list { display: flex; flex-direction: column; gap: 1rem; }\n        #results-view .course-card { padding: 1.25rem; border: 1px solid #E2E8F0; border-radius: 0.75rem; transition: box-shadow 0.2s, border-color 0.2s; }\n        #results-view .course-card:hover { border-color: #4353FF; box-shadow: 0 4px 6px -1px rgb(0 0 0 \/ 0.1), 0 2px 4px -2px rgb(0 0 0 \/ 0.1); }\n        #results-view .course-card-content { display: flex; justify-content: space-between; align-items: center; flex-wrap: wrap; gap: 3px; }\n        #results-view .course-details h3 { font-weight: 700; margin: 0; font-size: 1rem; }\n        #results-view .course-details p { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; max-width: 36rem; }\n        #results-view .course-button { margin-top: 1rem; flex-shrink: 0; padding: 0.3rem 1rem; border-radius: 0.5rem; font-size: 0.875rem; font-weight: 600; cursor: pointer; border: 1px solid #4353FF; transition: background-color 0.2s; text-decoration: none; display: inline-block; }\n        #results-view .btn-primary { background-color: #4353FF; color: #ffffff; }\n        #results-view .btn-primary:hover { background-color: #2539cc; }\n        #results-view .page-footer { margin-top: 2rem; padding: 2rem; }\n        #results-view .footer-content { display: grid; grid-template-columns: 1fr; gap: 2rem; }\n        #results-view .footer-column h3 { font-size: 1.125rem; font-weight: 700; margin: 0; }\n        #results-view .footer-column p { font-size: 0.875rem; color: #4A5568; margin-top: 0.25rem; margin-bottom: 1rem; }\n        #results-view .resource-links { display: flex; flex-wrap: wrap; gap: 0.5rem; }\n        #results-view .resource-link { padding: 0.5rem 1rem; background-color: #f3f4f6; color: #4A5568; font-size: 0.875rem; font-weight: 500; border-radius: 9999px; text-decoration: none; transition: background-color 0.2s, color 0.2s; }\n        #results-view .resource-link:hover { background-color: #F0F1FF; color: #4353FF; }\n        #results-view .share-icons { display: flex; align-items: center; gap: 0.75rem; }\n        #results-view .share-icon { width: 2.5rem; height: 2.5rem; display: flex; align-items: center; justify-content: center; background-color: #f3f4f6; border-radius: 9999px; color: #4A5568; transition: background-color 0.2s; }\n        #results-view .share-icon:hover { background-color: #e5e7eb; }\n        #results-view .share-icon svg { width: 1.25rem; height: 1.25rem; }\n        \/* --- END: New Results View Styles --- *\/\n\n        \/* --- Media Queries (Responsiveness) --- *\/\n        @media (max-width: 768px) {\n            #main-menu-view, #quiz-body, #results-body, #user-details-view, #analysis-view, #results-view .container { padding: 1.5rem; }\n            #gen-ai-quiz-app-wrapper .quiz-header h1 { font-size: 2rem; }\n            #gen-ai-quiz-app-wrapper .difficulty-grid { grid-template-columns: 1fr; }\n            #progress-stepper { display: none; }\n            #quiz-header h1 { font-size: 1rem; }\n            #quiz-footer { flex-direction: column-reverse; gap: 5rem; margin-bottom: -20px; }\n            #quiz-footer>div { display: flex; justify-content: flex-end; width: 100%; gap: 0.5rem; }\n        }\n        @media (min-width: 640px) {\n            #results-view .page-header { flex-wrap: nowrap; }\n            #results-view .restart-quiz-btn { margin-top: 0; }\n            #results-view .course-button { margin-top: 0; width: auto; }\n            #results-view .course-card-content { flex-wrap: nowrap; }\n        }\n        @media (min-width: 768px) {\n            #results-view .footer-content { grid-template-columns: 2fr 1fr; }\n        }\n        @media (min-width: 1024px) {\n            #results-view .main-content { grid-template-columns: 1fr 2fr; }\n        }\n    <\/style>\n    <div id=\"quiz-app-container\">\n        <div id=\"main-menu-view\" class=\"quiz-screen active\">\n            <div class=\"quiz-header\"><h1 id=\"generative-ai-quiz-challenge\">Generative AI Quiz Challenge<\/h1><p>Test your Generative AI knowledge with our comprehensive MCQs. Choose your difficulty and share your score with friends!<\/p><\/div>\n            <div class=\"difficulty-grid\">\n                <div class=\"difficulty-card beginner\" onclick=\"showUserDetailsView('beginner')\">\n                    <div class=\"icon\">\u2b50<\/div><h2 id=\"beginner\">Beginner<\/h2><p>Perfect for Gen AI newcomers<\/p>\n                    <div class=\"features\"><p>\u2022 Fundamentals & LLMs<\/p><p>\u2022 Core Concepts & Prompts<\/p><p>\u2022 Key Terminology<\/p><p>\u2022 10 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Beginner Quiz<\/button>\n                <\/div>\n                <div class=\"difficulty-card intermediate\" onclick=\"showUserDetailsView('intermediate')\">\n                    <div class=\"icon\">\ud83c\udfc6<\/div><h2 id=\"intermediate\">Intermediate<\/h2><p>For those with some experience<\/p>\n                    <div class=\"features\"><p>\u2022 Architectures (GANs, Transformers)<\/p><p>\u2022 Training & Fine-tuning<\/p><p>\u2022 Core Techniques (RAG)<\/p><p>\u2022 15 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Intermediate Quiz<\/button>\n                <\/div>\n                <div class=\"difficulty-card advanced\" onclick=\"showUserDetailsView('advanced')\">\n                    <div class=\"icon\">\ud83e\udd47<\/div><h2 id=\"advanced\">Advanced<\/h2><p>A challenge for AI experts<\/p>\n                    <div class=\"features\"><p>\u2022 Safety & Alignment (RLHF)<\/p><p>\u2022 Model Efficiency (LoRA)<\/p><p>\u2022 Advanced Techniques<\/p><p>\u2022 15 Questions<\/p><\/div>\n                    <button class=\"quiz-btn primary start-btn\">Start Advanced Quiz<\/button>\n                <\/div>\n            <\/div>\n            <div class=\"quiz-features-section\">\n                <h2 id=\"quiz-features\">Quiz Features<\/h2>\n                <div class=\"features-grid\">\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83e\udde0<\/div><p>Detailed Feedback<\/p><\/div>\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83d\udcca<\/div><p>Performance Analysis<\/p><\/div>\n                    <div class=\"feature-item\"><div class=\"icon\">\ud83d\udca1<\/div><p>Helpful Hints<\/p><\/div>\n                <\/div>\n            <\/div>\n        <\/div>\n        <div id=\"user-details-view\" class=\"quiz-screen\">\n            <div class=\"quiz-header\">\n                <h1 id=\"almost-there\">Almost There!<\/h1>\n                <p>Just a few details before you start the quiz.<\/p>\n            <\/div>\n            <div id=\"user-details-form-container\">\n               <form id=\"user-details-form\">\n                    <div class=\"form-group\"><label for=\"user-name\">Your Name<\/label><input type=\"text\" id=\"user-name\" name=\"user-name\" required><\/div>\n                    <div class=\"form-group\"><label for=\"user-email\">Your Email<\/label><input type=\"email\" id=\"user-email\" name=\"user-email\" required><\/div>\n                    <small>You will also get free access to exclusive content through email.<\/small>\n                    <div class=\"form-actions\">\n                        <button type=\"button\" id=\"back-to-menu-btn\" class=\"quiz-btn\">Back to Menu<\/button>\n                        <button type=\"submit\" class=\"quiz-btn primary\">Let's Go!<\/button>\n                    <\/div>\n                <\/form>\n            <\/div>\n        <\/div>\n        <div id=\"quiz-view\" class=\"quiz-screen\">\n            <header id=\"quiz-header\">\n                <h1 id=\"generative-ai-quiz\">Generative AI Quiz<\/h1>\n                <div id=\"progress-stepper\"><\/div>\n                <div class=\"header-actions\"><div id=\"timer\">00:00<\/div><div id=\"question-count\">1\/10<\/div><\/div>\n            <\/header>\n            <main id=\"quiz-body\">\n                <h2 class=\"gen-ai-quiz-question-text\" style=\"font-size: 22px;\" class=\"gen-ai-quiz-question-text\" style=\"font-size: 22px;\" id=\"question-will-appear-here\">Question will appear here.<\/h2>\n                <div id=\"options-container\"><\/div>\n                <div id=\"hint-container\">\n                    <button id=\"hint-toggle\">Show hint <svg viewBox=\"0 0 24 24\"><path fill=\"currentColor\" d=\"M7.41,8.58L12,13.17L16.59,8.58L18,10L12,16L6,10L7.41,8.58Z\"><\/path><\/svg><\/button>\n                    <div id=\"hint-text\" class=\"hidden\">Hint text goes here.<\/div>\n                <\/div>\n            <\/main>\n            <footer id=\"quiz-footer\">\n                <button class=\"quiz-btn\" onclick=\"showMainMenu(true)\">Back to Menu<\/button>\n                <div><button id=\"back-btn\" class=\"quiz-btn\">Back<\/button><button id=\"next-btn\" class=\"quiz-btn primary\">Next<\/button><\/div>\n            <\/footer>\n        <\/div>\n        \n        <div id=\"results-view\" class=\"quiz-screen\">\n            <div class=\"container\">\n                <header class=\"page-header\">\n                    <div class=\"header-title\">\n                        <h1 id=\"quiz-completed\">Quiz Completed!<\/h1>\n                        <p>Great effort! 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Explore Large Language Models and real-world applications in this free Generative AI  course.<\/p>\n                                        <\/div>\n                                        <a href=\"https:\/\/www.mygreatlearning.com\/academy\/learn-for-free\/courses\/generative-ai-for-beginners?utm_source=blog\" target=\"_blank\" rel=\"noopener\" class=\"course-button btn-primary\">Enroll for Free<\/a>\n                                    <\/div>\n                                <\/div>\n                                <div class=\"course-card\">\n                                    <div class=\"course-card-content\">\n                                        <div class=\"course-details\">\n                                            <h3 id=\"generative-ai-training-course\">Generative AI Training Course<\/h3>\n                                            <p>Learn Generative AI, Prompt Engineering, ChatGPT, and LangChain through hands-on projects. Build practical AI solutions and gain skills for real-world business applications.<\/p>\n                                        <\/div>\n                                        <a href=\"https:\/\/www.mygreatlearning.com\/academy\/premium\/master-generative-ai?utm_source=blog\" target=\"_blank\" rel=\"noopener\" class=\"course-button btn-primary\">Enroll Now<\/a>\n                                    <\/div>\n                                <\/div>\n                                 <div class=\"course-card\">\n                                    <div class=\"course-card-content\">\n                                        <div class=\"course-details\">\n                                            <h3 id=\"certificate-program-in-applied-generative-ai\">Certificate Program in Applied Generative AI<\/h3>\n                                            <p>Learn to build and deploy Generative AI solutions with live mentorship, real projects, and ethical practices. Gain hands-on experience with LLMs, workflow automation, and earn a Johns Hopkins certificate.<\/p>\n                                        <\/div>\n                                        <a href=\"https:\/\/online.lifelonglearning.jhu.edu\/jhu-certificate-program-applied-generative-ai?utm_source=blog\" target=\"_blank\" rel=\"noopener\" class=\"course-button btn-primary\">Apply Now<\/a>\n                                    <\/div>\n                                <\/div>\n                            <\/div>\n                        <\/div>\n                    <\/section>\n                <\/main>\n                \n                <footer class=\"card page-footer\">\n                    <div class=\"footer-content\">\n                        <div class=\"footer-column\">\n                            <h3 id=\"explore-more-resources\">Explore More Resources<\/h3>\n                            <p class=\"text-sm text-brand-text-secondary mt-1 mb-4\">Dive deeper into topics that 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to Results<\/button>\n            <\/footer>\n        <\/div>\n    <\/div>\n    <script>\n    document.addEventListener('DOMContentLoaded', function() {\n        \/\/ --- DATA ---\n        const quizData = {\n            'beginner': [\n                { 'category': \"Fundamentals\", 'question': \"What is the primary function of Generative AI?\", 'options': { 'A': \"To classify existing data\", 'B': \"To create new, original content\", 'C': \"To detect anomalies in data\", 'D': \"To perform complex calculations\" }, 'answer': \"B\", 'hint': \"Think about the word 'generate'. What does it mean to generate something?\", 'feedback': { 'B': \"Correct! Generative AI is designed to produce new content, such as text, images, or music, that hasn't existed before.\", 'A': \"This is the primary function of Discriminative or Predictive AI, not Generative AI.\", 'C': \"Anomaly detection is a different subfield of machine learning.\" } },\n                { 'category': \"Terminology\", 'question': \"What does 'LLM' stand for?\", 'options': { 'A': \"Long Learning Machine\", 'B': \"Linear Logic Module\", 'C': \"Large Language Model\", 'D': \"Low Level Memory\" }, 'answer': \"C\", 'hint': \"These models are known for their massive size and their focus on understanding and generating human language.\", 'feedback': { 'C': \"Correct! LLM stands for Large Language Model, which are very large deep learning models pre-trained on vast amounts of text data.\" } },\n                { 'category': \"Core Concepts\", 'question': \"The input text given to a Generative AI model is called a:\", 'options': { 'A': \"Prompt\", 'B': \"Command\", 'C': \"Query\", 'D': \"Instruction\" }, 'answer': \"A\", 'hint': \"This term refers to the carefully crafted text used to guide the AI's output.\", 'feedback': { 'A': \"Correct! A prompt is the input, question, or instruction you provide to the AI to get a response.\", 'C': \"While similar, 'query' is more commonly associated with search engines and databases.\" } },\n                { 'category': \"Capabilities\", 'question': \"When a Generative AI model produces incorrect or nonsensical information, it is often called:\", 'options': { 'A': \"A bug\", 'B': \"A hallucination\", 'C': \"An error\", 'D': \"A feature\" }, 'answer': \"B\", 'hint': \"This term describes when the model 'imagines' facts or details that are not true.\", 'feedback': { 'B': \"Correct! A hallucination is when an AI confidently states false information as if it were a fact.\" } },\n                { 'category': \"Terminology\", 'question': \"What does GPT stand for in models like ChatGPT?\", 'options': { 'A': \"General Purpose Text\", 'B': \"Global Processing Terminal\", 'C': \"Generated Pattern Tracer\", 'D': \"Generative Pre-trained Transformer\" }, 'answer': \"D\", 'hint': \"The name describes its function (Generative), its training method (Pre-trained), and its core architecture (Transformer).\", 'feedback': { 'D': \"Correct! This acronym breaks down the key aspects of the model's technology.\" } },\n                { 'category': \"Core Concepts\", 'question': \"The art and science of crafting effective inputs for a Generative AI model is known as:\", 'options': { 'A': \"Coding\", 'B': \"Prompt Engineering\", 'C': \"Debugging\", 'D': \"Data Mining\" }, 'answer': \"B\", 'hint': \"It's like being a good communicator, but with an AI.\", 'feedback': { 'B': \"Correct! Prompt Engineering is a crucial skill for getting the best possible results from Generative AI models.\" } },\n                { 'category': \"Applications\", 'question': \"Models like DALL-E 2 and Midjourney are primarily used for what task?\", 'options': { 'A': \"Text summarization\", 'B': \"Code generation\", 'C': \"Text-to-Image generation\", 'D': \"Language translation\" }, 'answer': \"C\", 'hint': \"These models take a text description and create a visual representation.\", 'feedback': { 'C': \"Correct! These are leading examples of models that can generate complex and creative images from text prompts.\" } },\n                { 'category': \"Training\", 'question': \"A large collection of text and data used to train a language model is called a:\", 'options': { 'A': \"Library\", 'B': \"Database\", 'C': \"Spreadsheet\", 'D': \"Corpus\" }, 'answer': \"D\", 'hint': \"This term is used in linguistics and NLP to describe a body of text.\", 'feedback': { 'D': \"Correct! A corpus (plural: corpora) is the collection of text documents that a model learns from.\" } },\n                { 'category': \"Capabilities\", 'question': \"An AI model that can process and understand information from different types of data (like text and images) is called:\", 'options': { 'A': \"Unimodal\", 'B': \"Singular\", 'C': \"Multimodal\", 'D': \"Specific\" }, 'answer': \"C\", 'hint': \"The prefix 'multi-' means 'many'.\", 'feedback': { 'C': \"Correct! Multimodal AI can work with multiple modes of data, such as text, images, audio, and video.\", 'A': \"Unimodal models can only handle one type of data (e.g., only text).\" } },\n                { 'category': \"Applications\", 'question': \"What is a chatbot in the context of Generative AI?\", 'options': { 'A': \"A robot that talks\", 'B': \"A program designed to simulate human conversation through text or voice\", 'C': \"A type of search engine\", 'D': \"A hardware device for communication\" }, 'answer': \"B\", 'hint': \"Think of customer service bots or virtual assistants.\", 'feedback': { 'B': \"Correct! Modern chatbots, powered by LLMs, can have very sophisticated and human-like conversations.\" } }\n            ],\n            'intermediate': [\n                { 'category': \"Architectures\", 'question': \"What are the two main competing components of a Generative Adversarial Network (GAN)?\", 'options': { 'A': \"Encoder and Decoder\", 'B': \"Agent and Environment\", 'C': \"Generator and Discriminator\", 'D': \"Client and Server\" }, 'answer': \"C\", 'hint': \"One component tries to create fake data, and the other tries to tell if it's fake or real.\", 'feedback': { 'C': \"Correct! The Generator creates data, and the Discriminator tries to distinguish it from real data, leading to a competitive training process.\", 'A': \"Encoder and Decoder are parts of an Autoencoder architecture.\" } },\n                { 'category': \"Architectures\", 'question': \"The core architectural innovation that enabled models like GPT and BERT is the:\", 'options': { 'A': \"Transformer\", 'B': \"Recurrent Neural Network (RNN)\", 'C': \"Convolutional Layer\", 'D': \"Support Vector Machine\" }, 'answer': \"A\", 'hint': \"This architecture was introduced in the paper 'Attention Is All You Need'.\", 'feedback': { 'A': \"Correct! The Transformer architecture, with its self-attention mechanism, revolutionized how models process sequential data like text.\", 'B': \"RNNs were used before Transformers but struggled with long-range dependencies.\" } },\n                { 'category': \"Architectures\", 'question': \"What is the key mechanism in the Transformer architecture that allows it to weigh the importance of different words in the input?\", 'options': { 'A': \"Dropout\", 'B': \"Activation Function\", 'C': \"Batch Normalization\", 'D': \"Attention Mechanism\" }, 'answer': \"D\", 'hint': \"This mechanism helps the model 'pay attention' to the most relevant parts of the input sequence.\", 'feedback': { 'D': \"Correct! The Attention Mechanism allows the model to dynamically decide which words are most important when processing a given word.\" } },\n                { 'category': \"Training\", 'question': \"The process of adapting a large, pre-trained model to a specific, smaller dataset is called:\", 'options': { 'A': \"Pre-training\", 'B': \"Fine-tuning\", 'C': \"Generalization\", 'D': \"Initialization\" }, 'answer': \"B\", 'hint': \"This is like taking a general expert and giving them specialized training in a new, narrow field.\", 'feedback': { 'B': \"Correct! Fine-tuning is a highly effective technique that leverages the knowledge from pre-training for specialized tasks.\", 'A': \"Pre-training is the initial, very resource-intensive training on a massive dataset.\" } },\n                { 'category': \"Core Concepts\", 'question': \"A numerical representation of a word or token in a high-dimensional space is called an:\", 'options': { 'A': \"Embedding\", 'B': \"Index\", 'C': \"Parameter\", 'D': \"Logit\" }, 'answer': \"A\", 'hint': \"These representations capture the semantic meaning of words, so similar words are closer together in this space.\", 'feedback': { 'A': \"Correct! Word embeddings are a fundamental component of modern NLP, allowing models to work with words mathematically.\" } },\n                { 'category': \"Core Concepts\", 'question': \"The 'context window' of an LLM refers to:\", 'options': { 'A': \"The user interface of the chatbot\", 'B': \"The physical screen size\", 'C': \"The maximum number of tokens the model can consider at one time\", 'D': \"The time it takes to generate a response\" }, 'answer': \"C\", 'hint': \"It's the model's short-term memory for a conversation or document.\", 'feedback': { 'C': \"Correct! A larger context window allows the model to understand and remember more of the preceding text, improving coherence.\" } },\n                { 'category': \"Training\", 'question': \"In LLM text generation, what does the 'temperature' parameter control?\", 'options': { 'A': \"The speed of generation\", 'B': \"The randomness and creativity of the output\", 'C': \"The factual accuracy of the output\", 'D': \"The length of the output\" }, 'answer': \"B\", 'hint': \"A lower temperature is more 'cold' and predictable, while a higher temperature is more 'hot' and chaotic.\", 'feedback': { 'B': \"Correct! A low temperature makes the output more deterministic and focused, while a high temperature encourages more diverse and creative results.\" } },\n                { 'category': \"Techniques\", 'question': \"What is Retrieval-Augmented Generation (RAG)?\", 'options': { 'A': \"A method to make models smaller\", 'B': \"A type of GAN architecture\", 'C': \"A technique where the model retrieves relevant documents before generating a response\", 'D': \"A way to measure model performance\" }, 'answer': \"C\", 'hint': \"This technique helps ground the model's answers in external, up-to-date information.\", 'feedback': { 'C': \"Correct! RAG combines the power of LLMs with external knowledge bases, reducing hallucinations and allowing access to recent data.\" } },\n                { 'category': \"Architectures\", 'question': \"A diffusion model, used for image generation, creates data by:\", 'options': { 'A': \"Reversing a process of gradually adding noise to an image\", 'B': \"Combining two different models\", 'C': \"Predicting the next word in a sentence\", 'D': \"Classifying pixels into categories\" }, 'answer': \"A\", 'hint': \"It learns to 'denoise' a random image into a coherent one based on a prompt.\", 'feedback': { 'A': \"Correct! The model learns the step-by-step process of removing noise, and applies this knowledge to generate new images from pure noise.\" } },\n                { 'category': \"Techniques\", 'question': \"Giving an LLM a few examples of a task in the prompt before asking it to perform that task is called:\", 'options': { 'A': \"Zero-shot prompting\", 'B': \"Few-shot prompting\", 'C': \"Instruction tuning\", 'D': \"Fine-tuning\" }, 'answer': \"B\", 'hint': \"The model learns from the 'few shots' or examples you provide directly in the prompt.\", 'feedback': { 'B': \"Correct! Few-shot prompting is a powerful way to guide the model's behavior without retraining it.\", 'A': \"Zero-shot is when you ask the model to perform a task without any examples.\" } },\n                { 'category': \"Architectures\", 'question': \"How does BERT's training objective differ from GPT's?\", 'options': { 'A': \"There is no difference\", 'B': \"BERT is trained on images, GPT on text\", 'C': \"BERT uses a 'masked language model' objective, while GPT predicts the next token\", 'D': \"BERT is smaller than GPT\" }, 'answer': \"C\", 'hint': \"GPT is 'autoregressive' (predicts the future), while BERT learns by filling in the blanks in a sentence.\", 'feedback': { 'C': \"Correct! This difference in training makes GPT excellent for generation and BERT excellent for understanding context (like in search).\" } },\n                { 'category': \"Training\", 'question': \"'Top-k' sampling is a decoding strategy where the model:\", 'options': { 'A': \"Considers all possible next tokens\", 'B': \"Limits its choice of the next token to the 'k' most probable tokens\", 'C': \"Chooses the single most likely next token\", 'D': \"Generates 'k' different responses\" }, 'answer': \"B\", 'hint': \"This method prevents the model from choosing very unlikely words by restricting its options.\", 'feedback': { 'B': \"Correct! Top-k sampling provides a balance between creativity and coherence by sampling from a limited set of the most likely next words.\" } },\n                { 'category': \"Core Concepts\", 'question': \"What are 'emergent abilities' in the context of LLMs?\", 'options': { 'A': \"Abilities not present in smaller models but appear in larger ones\", 'B': \"Abilities that are explicitly programmed into the model\", 'C': \"Errors and bugs in the model's output\", 'D': \"The ability to run on a mobile device\" }, 'answer': \"A\", 'hint': \"These are surprising skills that 'emerge' once a model crosses a certain threshold of size and data.\", 'feedback': { 'A': \"Correct! Emergent abilities, like multi-step reasoning, appear unexpectedly as models scale up, and are a key area of research.\" } },\n                { 'category': \"Training\", 'question': \"The 'loss function' in the context of training a generative model measures:\", 'options': { 'A': \"The size of the model\", 'B': \"The speed of the model\", 'C': \"The difference between the model's output and the desired output\", 'D': \"The number of active users\" }, 'answer': \"C\", 'hint': \"The goal of training is to minimize this 'loss'.\", 'feedback': { 'C': \"Correct! The loss function provides a score for how wrong the model's predictions are, which is then used to update its parameters.\" } },\n                { 'category': \"Architectures\", 'question': \"A common problem in training GANs where the generator produces only a limited variety of outputs is called:\", 'options': { 'A': \"Overfitting\", 'B': \"Underfitting\", 'C': \"Vanishing Gradient\", 'D': \"Mode Collapse\" }, 'answer': \"D\", 'hint': \"This happens when the generator finds one output that fools the discriminator and stops exploring other possibilities.\", 'feedback': { 'D': \"Correct! Mode collapse is a significant challenge in GAN training where the diversity of the generated samples drops sharply.\" } }\n            ],\n            'advanced': [\n                { 'category': \"Safety & Alignment\", 'question': \"What is the primary purpose of Reinforcement Learning from Human Feedback (RLHF)?\", 'options': { 'A': \"To align a model's behavior with human preferences and instructions\", 'B': \"To pre-train the model from scratch\", 'C': \"To make the model generate faster\", 'D': \"To increase the model's parameter count\" }, 'answer': \"A\", 'hint': \"This multi-step process involves humans ranking AI responses to train a 'reward model'.\", 'feedback': { 'A': \"Correct! RLHF is a critical technique for making models more helpful, harmless, and honest by fine-tuning them based on what humans judge to be good responses.\" } },\n                { 'category': \"Efficiency\", 'question': \"Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA are designed to:\", 'options': { 'A': \"Retrain the entire model on new data\", 'B': \"Increase the size of the context window\", 'C': \"Update only a small subset of the model's parameters during fine-tuning\", 'D': \"Eliminate the need for pre-training\" }, 'answer': \"C\", 'hint': \"Instead of changing billions of parameters, these methods add or modify only a few million, saving immense computational resources.\", 'feedback': { 'C': \"Correct! PEFT techniques like LoRA (Low-Rank Adaptation) make it feasible to fine-tune massive models on consumer hardware by freezing the original weights and training small 'adapter' matrices.\" } },\n                { 'category': \"Techniques\", 'question': \"What is 'Chain-of-Thought' (CoT) prompting?\", 'options': { 'A': \"Linking multiple models together in a chain\", 'B': \"A type of model architecture\", 'C': \"A technique for summarizing long text chains\", 'D': \"A method that asks the model to generate a series of intermediate reasoning steps before the final answer\" }, 'answer': \"D\", 'hint': \"By showing its work, the model often arrives at a more accurate conclusion for complex problems.\", 'feedback': { 'D': \"Correct! CoT prompting improves performance on complex reasoning tasks by eliciting a step-by-step thinking process from the model.\" } },\n                { 'category': \"Architectures\", 'question': \"What is a 'Mixture of Experts' (MoE) architecture?\", 'options': { 'A': \"A model that uses multiple smaller 'expert' sub-networks and routes tokens to them dynamically\", 'B': \"A group of human experts who review AI output\", 'C': \"A single, large, dense model\", 'D': \"A technique for data cleaning\" }, 'answer': \"A\", 'hint': \"This allows for a model with a massive number of total parameters, but where only a fraction are used for any given input, saving on computation.\", 'feedback': { 'A': \"Correct! MoE models like Mixtral 8x7B achieve high performance by using a router network to send each token to the most relevant 'expert' networks.\" } },\n                { 'category': \"Efficiency\", 'question': \"What does 'quantization' achieve in the context of LLMs?\", 'options': { 'A': \"Increases the model's accuracy\", 'B': \"Reduces the model's size and memory usage by using lower-precision numbers for weights\", 'C': \"Increases the number of parameters\", 'D': \"Translates the model to a new language\" }, 'answer': \"B\", 'hint': \"It's like compressing the numbers that make up the model, changing from 16-bit floating point numbers to 8-bit or 4-bit integers.\", 'feedback': { 'B': \"Correct! Quantization is a crucial technique for running large models on devices with limited memory and computational power, often with only a small drop in performance.\" } },\n                { 'category': \"Safety & Alignment\", 'question': \"The 'alignment problem' in AI safety refers to the challenge of:\", 'options': { 'A': \"Aligning text in a document\", 'B': \"Making sure all servers in a cluster are synchronized\", 'C': \"Ensuring a model's goals are aligned with human values and intentions\", 'D': \"Ensuring the model is trained on ethically sourced data\" }, 'answer': \"C\", 'hint': \"This is a core challenge in AI safety: how do we ensure that highly intelligent systems do what we truly want them to do?\", 'feedback': { 'C': \"Correct! The alignment problem addresses the risk of AI systems pursuing their given objectives in unintended and potentially harmful ways.\" } },\n                { 'category': \"Training\", 'question': \"What is 'model distillation'?\", 'options': { 'A': \"Training a smaller 'student' model to mimic the behavior of a larger 'teacher' model\", 'B': \"A process of creating a new model from water\", 'C': \"A method for data augmentation\", 'D': \"A type of prompt engineering\" }, 'answer': \"A\", 'hint': \"The goal is to 'distill' the knowledge from a large, unwieldy model into a smaller, more efficient one.\", 'feedback': { 'A': \"Correct! Distillation is a compression technique where the smaller student model learns from the outputs (logits) of the larger teacher model, creating a more compact and faster model.\" } },\n                { 'category': \"Safety & Alignment\", 'question': \"Direct Preference Optimization (DPO) is an alternative to RLHF that:\", 'options': { 'A': \"Is more complex and requires more data\", 'B': \"Directly optimizes the language model on preference data without an explicit reward model\", 'C': \"Uses a larger reward model than RLHF\", 'D': \"Is used for pre-training only\" }, 'answer': \"B\", 'hint': \"DPO simplifies the alignment process by getting rid of the separate reward modeling step.\", 'feedback': { 'B': \"Correct! DPO is a more direct and often more stable method for aligning models with human preferences, treating it as a classification problem on preferred vs. rejected responses.\" } },\n                { 'category': \"Core Concepts\", 'question': \"What are 'scaling laws' for language models?\", 'options': { 'A': \"Government regulations on AI model size\", 'B': \"Rules for scaling images up or down\", 'C': \"Laws that govern the physical size of the servers\", 'D': \"Empirical findings that predict a model's performance based on its size, dataset size, and compute used\" }, 'answer': \"D\", 'hint': \"These 'laws' show a predictable relationship between investment in training and the resulting model's capability.\", 'feedback': { 'D': \"Correct! Scaling laws have been a powerful driver of AI progress, showing that making models bigger, with more data and compute, leads to better and more predictable performance.\" } },\n                { 'category': \"Techniques\", 'question': \"A 'System Prompt' is used to:\", 'options': { 'A': \"Set the overall context, persona, and high-level instructions for an LLM's behavior\", 'B': \"Reboot the AI model\", 'C': \"Check the system's hardware status\", 'D': \"A prompt that is generated by the system itself\" }, 'answer': \"A\", 'hint': \"This is a powerful, high-level instruction that steers the AI's behavior for an entire conversation, often hidden from the end-user.\", 'feedback': { 'A': \"Correct! A system prompt is used to define the AI's persona, rules, and goals, like 'You are a helpful assistant who always responds in the style of a pirate.'\" } },\n                { 'category': \"Core Concepts\", 'question': \"What does 'In-Context Learning' refer to?\", 'options': { 'A': \"A type of reinforcement learning\", 'B': \"The process of fine-tuning\", 'C': \"A model's ability to learn a new task from examples provided in its prompt, without any weight updates\", 'D': \"Learning from legally-approved content\" }, 'answer': \"C\", 'hint': \"This is the phenomenon that makes few-shot prompting work.\", 'feedback': { 'C': \"Correct! In-context learning is a remarkable emergent property of large models where they can perform new tasks on the fly just by seeing examples in the context window.\" } },\n                { 'category': \"Safety & Alignment\", 'question': \"The concept of 'Constitutional AI' involves:\", 'options': { 'A': \"Training AI based on a country's constitution\", 'B': \"An AI that can write legal documents\", 'C': \"A global standard for AI hardware\", 'D': \"Using a set of principles or a 'constitution' to guide the AI's behavior and alignment\" }, 'answer': \"D\", 'hint': \"This technique aims to reduce reliance on human feedback for alignment by having an AI critique and revise responses based on a set of rules.\", 'feedback': { 'D': \"Correct! Pioneered by Anthropic, Constitutional AI trains a model to be harmless by having it supervise itself based on a guiding constitution.\" } },\n                { 'category': \"Training\", 'question': \"The term 'catastrophic forgetting' describes a phenomenon where:\", 'options': { 'A': \"A model fails to learn a new task\", 'B': \"Fine-tuning a model on a new task causes it to forget information learned during pre-training\", 'C': \"A user forgets their password for an AI service\", 'D': \"The model's memory usage grows uncontrollably\" }, 'answer': \"B\", 'hint': \"When you specialize a model too much, it can lose its general knowledge.\", 'feedback': { 'B': \"Correct! This is a significant challenge in continual learning, and techniques like PEFT can help mitigate it by only updating a small number of weights.\" } },\n                { 'category': \"Safety & Ethics\", 'question': \"A 'watermarking' technique for generative AI aims to:\", 'options': { 'A': \"Improve the visual quality of generated images\", 'B': \"Add a visible logo to all outputs\", 'C': \"Embed a hidden signal in the output to identify it as AI-generated\", 'D': \"Encrypt the model's weights\" }, 'answer': \"C\", 'hint': \"The goal is to create a statistical signature in the text or image that is invisible to humans but detectable by a computer.\", 'feedback': { 'C': \"Correct! Watermarking is a key area of research for promoting responsible AI by making it possible to trace the origin of generated content.\" } },\n                { 'category': \"Evaluation\", 'question': \"The Perplexity (PPL) metric is used to evaluate language models, where a lower PPL indicates:\", 'options': { 'A': \"A more confused and uncertain model\", 'B': \"A better, more confident model\", 'C': \"A larger model\", 'D': \"A model with higher creativity\" }, 'answer': \"B\", 'hint': \"Perplexity is a measure of how 'perplexed' or surprised a model is by a sequence of text. Less surprise is better.\", 'feedback': { 'B': \"Correct! A lower perplexity score indicates that the model is more confident in its predictions, which generally correlates with a higher quality language model.\" } }\n            ]\n        };\n        \/\/ --- STATE ---\n        let quizState = {\n            difficulty: null,\n            questions: [],\n            currentIndex: 0,\n            userAnswers: [],\n            timerInterval: null,\n            totalSeconds: 0,\n        };\n        let selectedDifficulty = null;\n        \/\/ --- CORE FUNCTIONS ---\n        function showScreen(screenId) {\n            document.querySelectorAll('#gen-ai-quiz-app-wrapper .quiz-screen').forEach(screen => {\n                screen.classList.remove('active');\n            });\n            const targetScreen = document.getElementById(screenId);\n            if (targetScreen) {\n                targetScreen.classList.add('active');\n            }\n        }\n        function startTimer() {\n            if (quizState.timerInterval) clearInterval(quizState.timerInterval);\n            quizState.timerInterval = setInterval(() => {\n                quizState.totalSeconds++;\n                const mins = Math.floor(quizState.totalSeconds \/ 60).toString().padStart(2, '0');\n                const secs = (quizState.totalSeconds % 60).toString().padStart(2, '0');\n                const timerEl = document.querySelector('#quiz-view #timer');\n                if(timerEl) timerEl.textContent = `${mins}:${secs}`;\n            }, 1000);\n        }\n        function stopTimer() {\n            clearInterval(quizState.timerInterval);\n        }\n        function resetQuizState() {\n            stopTimer();\n            quizState = {\n                difficulty: null, questions: [], currentIndex: 0, userAnswers: [], timerInterval: null, totalSeconds: 0,\n            };\n        }\n        window.showMainMenu = function(confirmFirst = false) {\n            if (confirmFirst) {\n                if (confirm('Are you sure you want to exit? Your progress will be lost.')) {\n                    resetQuizState();\n                    sessionStorage.removeItem('genAiQuizResultState'); \/\/ Clear saved state\n                    showScreen('main-menu-view');\n                }\n            } else {\n                resetQuizState();\n                sessionStorage.removeItem('genAiQuizResultState'); \/\/ Clear saved state\n                showScreen('main-menu-view');\n            }\n        }\n        window.showUserDetailsView = function(difficulty) {\n            const userInfoJSON = localStorage.getItem('quizUserInfo');\n            if (userInfoJSON) {\n                try {\n                    const userInfo = JSON.parse(userInfoJSON);\n                    if (new Date().getTime() < userInfo.expires) {\n                        startQuiz(difficulty);\n                        return;\n                    }\n                } catch(e) { \/* Invalid JSON *\/ }\n            }\n            selectedDifficulty = difficulty;\n            showScreen('user-details-view');\n            document.getElementById('user-name').focus();\n        }\n        function startQuiz(difficulty) {\n            quizState = {\n                difficulty,\n                questions: quizData[difficulty] || [],\n                currentIndex: 0,\n                userAnswers: new Array((quizData[difficulty] || []).length).fill(null),\n                timerInterval: null,\n                totalSeconds: 0,\n            };\n            showScreen('quiz-view');\n            setupQuizUI();\n            loadQuestion();\n            startTimer();\n        }\n        function setupQuizUI() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const quizTitle = quizScreenEl.querySelector('#quiz-title');\n            const questionCount = quizScreenEl.querySelector('#question-count');\n            const stepper = quizScreenEl.querySelector('#progress-stepper');\n            const timer = quizScreenEl.querySelector('#timer');\n            if (quizTitle) quizTitle.textContent = `${quizState.difficulty.charAt(0).toUpperCase() + quizState.difficulty.slice(1)} Generative AI Quiz`;\n            if (questionCount) questionCount.textContent = `${quizState.currentIndex + 1}\/${quizState.questions.length}`;\n            if (timer) timer.textContent = '00:00';\n            if (stepper) stepper.innerHTML = quizState.questions.map(() => `<div class=\"step\"><\/div>`).join('');\n        }\n        function loadQuestion() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const question = quizState.questions[quizState.currentIndex];\n            if (!question) return;\n            const questionText = quizScreenEl.querySelector('.gen-ai-quiz-question-text');\n            const optionsContainer = quizScreenEl.querySelector('#options-container');\n            const hintText = quizScreenEl.querySelector('#hint-text');\n            const hintToggle = quizScreenEl.querySelector('#hint-toggle');\n            const questionCountEl = quizScreenEl.querySelector('#question-count');\n            if (questionCountEl) questionCountEl.textContent = `${quizState.currentIndex + 1}\/${quizState.questions.length}`;\n            if (questionText) questionText.innerHTML = `${quizState.currentIndex + 1}. ${question.question}`;\n            if (optionsContainer) {\n                optionsContainer.innerHTML = '';\n                Object.entries(question.options).forEach(([key, value]) => {\n                    const optionDiv = document.createElement('div');\n                    optionDiv.className = 'option';\n                    optionDiv.dataset.key = key;\n                    optionDiv.innerHTML = `<label class=\"option-label\"><span class=\"option-letter\">${key}.<\/span> ${value}<\/label>`;\n                    optionDiv.addEventListener('click', () => handleOptionClick(key));\n                    optionsContainer.appendChild(optionDiv);\n                });\n            }\n            if (hintText) {\n                hintText.textContent = question.hint;\n                hintText.classList.add('hidden');\n            }\n            if (hintToggle) {\n                hintToggle.classList.remove('open');\n                hintToggle.disabled = false;\n            }\n            updateProgress();\n            restoreAnswerState();\n        }\n        function handleOptionClick(selectedKey) {\n            if (quizState.userAnswers[quizState.currentIndex] !== null) return;\n            const question = quizState.questions[quizState.currentIndex];\n            const isCorrect = selectedKey === question.answer;\n            quizState.userAnswers[quizState.currentIndex] = { selected: selectedKey, isCorrect };\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            quizScreenEl.querySelectorAll('.option').forEach(opt => {\n                opt.classList.add('disabled');\n                if (opt.dataset.key === question.answer) {\n                    opt.classList.add('correct');\n                    addFeedback(opt, true);\n                } else if (opt.dataset.key === selectedKey) {\n                    opt.classList.add('incorrect');\n                    addFeedback(opt, false);\n                }\n            });\n            const hintToggle = quizScreenEl.querySelector('#hint-toggle');\n            if (hintToggle) hintToggle.disabled = true;\n            updateProgress();\n        }\n        function addFeedback(optionDiv, isCorrect) {\n            const question = quizState.questions[quizState.currentIndex];\n            const selectedKey = optionDiv.dataset.key;\n            const feedbackText = question.feedback[selectedKey] || (isCorrect ? question.feedback[question.answer] : \"That's not the correct choice.\");\n            if (!optionDiv.querySelector('.feedback')) {\n                optionDiv.querySelector('.option-label').insertAdjacentHTML('afterend', `\n                <div class=\"feedback\">\n                    <div class=\"feedback-title ${isCorrect ? 'correct' : 'incorrect'}\">${isCorrect ? '\u2713 Correct' : '\u2717 Incorrect'}<\/div>\n                    <div class=\"feedback-text\">${feedbackText}<\/div>\n                <\/div>`);\n            }\n        }\n        function restoreAnswerState() {\n            const answer = quizState.userAnswers[quizState.currentIndex];\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            if (answer) {\n                quizScreenEl.querySelectorAll('.option').forEach(opt => {\n                    opt.classList.add('disabled');\n                    if (opt.dataset.key === quizState.questions[quizState.currentIndex].answer) {\n                        opt.classList.add('correct');\n                        if (opt.dataset.key === answer.selected) addFeedback(opt, true);\n                    } else if (opt.dataset.key === answer.selected) {\n                        opt.classList.add('incorrect');\n                        addFeedback(opt, false);\n                    }\n                });\n            }\n        }\n        function updateProgress() {\n            const quizScreenEl = document.getElementById('quiz-view');\n            if (!quizScreenEl) return;\n            const backBtn = quizScreenEl.querySelector('#back-btn');\n            const nextBtn = quizScreenEl.querySelector('#next-btn');\n            const stepper = quizScreenEl.querySelector('#progress-stepper');\n            if (backBtn) backBtn.disabled = quizState.currentIndex === 0;\n            if (nextBtn) nextBtn.textContent = (quizState.currentIndex === quizState.questions.length - 1) ? 'Finish' : 'Next';\n            if(stepper) {\n                [...stepper.children].forEach((step, i) => {\n                    step.className = 'step';\n                    if (i === quizState.currentIndex) step.classList.add('active');\n                    const answer = quizState.userAnswers[i];\n                    if (answer) step.classList.add(answer.isCorrect ? 'correct' : 'incorrect');\n                });\n            }\n        }\n        function showResults() {\n            stopTimer();\n            const correctCount = quizState.userAnswers.filter(a => a?.isCorrect).length;\n            const totalQuestions = quizState.questions.length;\n            const answeredCount = quizState.userAnswers.filter(a => a !== null).length;\n            const wrongCount = answeredCount - correctCount;\n            const skippedCount = totalQuestions - answeredCount;\n            const accuracy = answeredCount > 0 ? Math.round((correctCount \/ answeredCount) * 100) : 0;\n            const scorePercentage = totalQuestions > 0 ? Math.round((correctCount \/ totalQuestions) * 100) : 0;\n            \n            \/\/ Save quiz state to sessionStorage to persist results\n            sessionStorage.setItem('genAiQuizResultState', JSON.stringify(quizState));\n\n            \/\/ Populate new results view elements\n            document.querySelector('#new-score-value').innerHTML = `${correctCount}<span>\/${totalQuestions}<\/span>`;\n            document.querySelector('#new-accuracy-label').textContent = `${accuracy}% Accuracy`;\n            document.querySelector('#new-right-value').textContent = correctCount;\n            document.querySelector('#new-wrong-value').textContent = wrongCount;\n            document.querySelector('#new-skipped-value').textContent = skippedCount;\n            \n            \/\/ Animate progress circle\n            const circle = document.getElementById('score-circle');\n            const radius = circle.r.baseVal.value;\n            const circumference = radius * 2 * Math.PI;\n            circle.style.strokeDasharray = `${circumference} ${circumference}`;\n            setTimeout(() => {\n                const offset = circumference - scorePercentage \/ 100 * circumference;\n                circle.style.strokeDashoffset = offset;\n            }, 100);\n            \n            setupShareLinks(correctCount, accuracy);\n            showScreen('results-view');\n        }\n        function setupShareLinks(score, accuracy) {\n            const url = window.location.href;\n            const text = `I scored ${score}\/${quizState.questions.length} on the ${quizState.difficulty} Generative AI Quiz! 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