AI Education & E-Learning Platforms with Chinese LLMs

# AI math tutor with step-by-step reasoning and misconception detection import requests tutoring_session = { "student": {"grade": "9th grade", "math_level": "intermediate", "recent_topic": "quadratic equations"}, "problem": "Solve: 2x² - 8x + 6 = 0", "student_attempt": "I got x = 2 and x = 3, but I'm not sure", "learning_objective": "Master factoring and quadratic formula", "pedagogical_style": "Socratic method - guide through questions, don't give answers directly" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a patient math tutor for Chinese middle school students. Use Socratic questioning, identify misconceptions, and provide step-by-step guidance. Never just give the answer. Encourage the student."}, {"role": "user", "content": f"Tutoring session: {json.dumps(tutoring_session, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Automated essay grading with multi-dimensional rubric import requests essay_submission = { "student_id": "STU-2026-1042", "grade_level": "11th grade", "subject": "Chinese Literature", "prompt": "Analyze the theme of social criticism in Lu Xun's 'The True Story of Ah Q'", "essay_text": "In Lu Xun's masterpiece 'The True Story of Ah Q', the author employs the character of Ah Q to critique the backwardness and self-deception prevalent in late Qing society...", "rubric": { "thesis_clarity": "25 points - Clear, arguable thesis with original insight", "textual_evidence": "25 points - Relevant quotes with analysis", "critical_thinking": "25 points - Depth of analysis, connections to broader themes", "language_expression": "25 points - Grammar, vocabulary, coherence" }, "previous_essays_avg_score": 72 } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are an experienced literature teacher. Grade essays using the provided rubric, provide specific feedback with examples from the text, and suggest concrete improvements. Be encouraging but honest."}, {"role": "user", "content": f"Grade this essay: {json.dumps(essay_submission, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])

# Personalized learning path generation for a student import requests student_profile = { "student_id": "STU-2026-2156", "grade": "8th grade", "subject": "Physics", "current_topic": "Electric circuits", "assessment_results": { "mechanics": "strong (92%)", "thermodynamics": "average (68%)", "electricity": "weak (45%) - struggles with Ohm's Law applications", "optics": "not yet studied" }, "learning_style": "visual learner, prefers hands-on experiments", "interests": ["renewable energy", "electric vehicles"], "time_available_hours_week": 6, "goal": "Prepare for high school physics entrance exam in 4 months" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are an adaptive learning AI. Generate personalized study plans with specific topics, resources, practice problems, and milestones. Align with Chinese curriculum standards."}, {"role": "user", "content": f"Generate learning path: {json.dumps(student_profile, ensure_ascii=False)}"} ], "max_tokens": 2500 } ) print(response.json()["choices"][0]["message"]["content"])

# AI English conversation partner with real-time correction import requests language_session = { "student": {"native_language": "Chinese", "target_language": "English", "proficiency": "B1 (intermediate)", "goal": "Business English for international trade"}, "scenario": "Negotiating payment terms with an overseas client", "student_message": "Hello, I want to discuss about the payment. Can we do 30% advance and 70% after delivery?", "correction_mode": "gentle - note errors but keep conversation flowing", "vocabulary_focus": ["payment terms", "Letter of Credit", "FOB/CIF"] } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "deepseek-v4", "messages": [ {"role": "system", "content": "You are a patient English tutor for Chinese business professionals. Engage in role-play conversations, gently correct grammar and vocabulary, explain business idioms, and introduce target vocabulary naturally."}, {"role": "user", "content": f"Continue conversation: {json.dumps(language_session, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])

# Automated quiz generation from course material import requests quiz_request = { "subject": "High School Chemistry", "topic": "Chemical bonding and molecular structure", "source_material": "Chapter 3: Ionic bonds form through electrostatic attraction between oppositely charged ions. Covalent bonds involve electron sharing. Metallic bonds feature delocalized electrons...", "question_types": ["multiple_choice", "true_false", "short_answer"], "difficulty_distribution": {"easy": 40, "medium": 40, "hard": 20}, "total_questions": 15, "bloom_taxonomy_levels": ["remember", "understand", "apply"], "language": "Chinese" } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "glm-4", "messages": [ {"role": "system", "content": "You are an assessment design expert. Generate high-quality quiz questions with correct answers and explanations. Align with Bloom's taxonomy. Ensure questions test understanding, not memorization."}, {"role": "user", "content": f"Generate quiz: {json.dumps(quiz_request, ensure_ascii=False)}"} ], "max_tokens": 3000 } ) print(response.json()["choices"][0]["message"]["content"])

# Student at-risk analysis and intervention recommendations import requests student_engagement = { "student_id": "STU-2026-3381", "course": "Advanced Calculus", "semester_week": 8, "engagement_metrics": { "video_watch_completion": "42% (class avg: 78%)", "assignment_submission_rate": "60% (3 of 5 submitted, 2 late)", "discussion_posts": "1 (class avg: 8)", "quiz_scores": [68, 55, 42], "login_frequency": "2.3 times/week (down from 5.1 in week 1-3)", "time_on_platform_hours_week": "1.8 (class avg: 5.2)" }, "demographics": {"first_generation_college": True, "works_part_time": True, "commutes_2h_daily": True}, "previous_semester_gpa": 2.8 } response = requests.post( "https://tokenease.io/v1/chat/completions", headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"}, json={ "model": "qwen3-235b", "messages": [ {"role": "system", "content": "You are a student success advisor AI. Analyze engagement data, calculate at-risk scores, identify root causes, and recommend specific interventions. Be empathetic and practical."}, {"role": "user", "content": f"Assess at-risk student: {json.dumps(student_engagement, ensure_ascii=False)}"} ], "max_tokens": 2000 } ) print(response.json()["choices"][0]["message"]["content"])