This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built AcademAI for someone very close to my heart: my wife.

She is currently completing her undergraduate thesis in S1 PAUD (Pendidikan Anak Usia Dini — Early Childhood Education) in Indonesia while also managing our home and daily family life. Her research investigates how natural loose-parts media affects fine motor development in children aged 5–6 years.

Every evening, I would sit beside her at our kitchen table and watch the real, human friction of academic research:

1. The Fear of Fabricated Sources: General AI assistants frequently invent scientific literature. When she asked for early childhood learning frameworks, chatbots confidently generated non-existent papers attributed to Jean Piaget, Lev Vygotsky, and Maria Montessori.
2. Disconnection from Local Curriculum Standards: Global AI models have no working knowledge of Indonesian statutory early childhood frameworks, particularly STPPA (Permendikbudristek No. 5/2022) or the national developmental rubrics (BB = Belum Berkembang, MB = Mulai Berkembang, BSH = Berkembang Sesuai Harapan, BSB = Berkembang Sangat Baik).
3. Translating Classroom Statistics into Academic Prose: In her Classroom Action Research (PTK — Penelitian Tindakan Kelas), computing paired sample t-tests and Hake's Normalized Gain (N-Gain), then articulating what those numbers actually mean in formal Indonesian academic prose for Chapter IV (Bab IV), was a constant bottleneck.
4. Drafting Anxiety and Revision Fatigue: Staring at a blank document trying to structure an inverted-pyramid background (Latar Belakang) while juggling household duties drained her energy.

What AcademAI Does

AcademAI is an open-source research companion designed to keep the student firmly in control while removing mechanical roadblocks:

• Traceable Literature Search via SerpApi: Searches Google Scholar in real time for genuine, peer-reviewed articles and supplies verified titles, authors, and links so the student can inspect the original text before citing it.
• Citation Format & Metadata Inspection: Audits in-text citations (APA 7th) and DOI identifiers against retrieved paper metadata to help students catch misattributed quotes or missing publication years.
• PTK Statistics & Interpretation: Takes raw pretest and posttest developmental assessment scores, calculates Paired Sample t-Tests and Hake (1999) N-Gain categories (Tinggi, Sedang, Rendah), and generates a transparent narrative draft explaining the pedagogical implications for Bab IV.
• Academic Restructuring & Paraphrasing: Provides syntactic transformations (such as nominalization and formal impersonal passive voice) to help students convey their own arguments clearly and maintain formal academic tone.
• Modular Knowledge Framework: Built and tested primarily around my wife's PAUD research, with an extensible DISCIPLINES Registry that provides foundational theory templates for 11 other faculties (Education, Economics, Law, Health, Computer Science, etc.).

"Having an assistant that actually finds real Indonesian journals and explains what my pretest-posttest N-Gain scores mean in proper academic wording saved me hours of second-guessing at night."

— My wife, testing the first working draft of AcademAI.

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Demo

• 🌐 Live Web Application: https://academicai-production-a41d.up.railway.app
• 📡 Live Health Endpoint: https://academicai-production-a41d.up.railway.app/healthz

The Five-Step Research Flow (Demonstrated in Early Childhood Education)

To see how the application works in practice, follow this real research flow:

1. Step 1: Enter Topic & Friction Point (Tab 1 — Chat) Select Pendidikan Anak Usia Dini (PAUD). Enter the research question: "Bagaimana pemanfaatan media loose parts berbasis alam untuk menstimulasi motorik halus anak usia 5-6 tahun?"
2. Step 2: Inspect Traceable Literature AcademAI queries Google Scholar via SerpApi. Before answering, it lists the discovered peer-reviewed articles with direct links, authors, and publication years so the user can verify them.
3. Step 3: Analyze Classroom Data (Tab 5 — Statistics) Enter classroom action research scores (e.g., Pretest: [50, 55, 60, 52, 58], Posttest: [78, 85, 82, 80, 88], Max Score: 100). AcademAI computes the mean difference, t-statistic, and Hake N-Gain (e.g., 0.58 — Category Sedang).
4. Step 4: Draft Empirical Discussion (Bab IV) The system generates an initial academic discussion linking the statistical gains back to Piaget's sensorimotor/pre-operational theory and the STPPA motor milestones.
5. Step 5: Review & Export to Word (Tab 2 / DOCX Export) The student reviews, refines, and exports the formatted document as a .docx file for offline editing and thesis advisor review.

# Verify the live production deployment
curl -s https://academicai-production-a41d.up.railway.app/healthz

{
  "status": "ok",
  "server": "AcademAI Universal Academic Engine",
  "discipline": "Universal Academic Research (Multi-Disciplinary)",
  "features": [
    "memory_context",
    "pdf_parser",
    "plagiarism_checker",
    "google_scholar",
    "zotero_sync",
    "full_generator",
    "citation_validator",
    "academic_stats",
    "dataviz_mcp"
  ],
  "models": [
    "gemini-3.1-flash-lite",
    "gemini-3.5-flash-lite",
    "gemini-3.7-flash",
    "gemini-3.8-flash"
  ]
}

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Code

yandri918 / academic_ai

🎓 AcademAI — An Open-Source Research Companion Built for My Wife's PAUD Thesis

🎃 Submission for Hacktoberfest Weekend Challenge: Build for a Friend
Dedicated to: Helping my wife complete her undergraduate thesis in Early Childhood Education (S1 PAUD), powered by an open-source Node.js agent harness, Google Gemini, and SerpApi for grounded literature search.

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🌐 Live Web Application

• Live Production URL: https://academicai-production-a41d.up.railway.app
• Health Check Endpoint: https://academicai-production-a41d.up.railway.app/healthz

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💡 The Story Behind AcademAI

Every evening, I watched my wife sit at our kitchen table overwhelmed by the friction of writing her undergraduate thesis in Early Childhood Education (S1 PAUD). Standard commercial AI tools:

1. Fabricated academic sources (inventing fake citations attributed to Piaget, Vygotsky, or Montessori).
2. Knew nothing about local education standards like Indonesia's STPPA (Permendikbudristek No. 5/2022) or national developmental rubrics (BB / MB / BSH / BSB).
3. Left her stuck on empirical calculations like Paired Sample…

View on GitHub

High-Level System Architecture

┌─────────────────────────────────────────────────────────────────┐
│     Client Layer: Vanilla HTML5 · CSS3 · ES2022 (Zero Build)     │
│   5 Research Tabs: Chat · Generator · Validator · Audit · Stats  │
└────────────────────────────────┬────────────────────────────────┘
                                 │ REST API
┌────────────────────────────────▼────────────────────────────────┐
│      OPEN-SOURCE AGENT HARNESS (Standalone Node.js / Express)   │
│                                                                 │
│   ┌────────────────────────────────────────────────────────┐    │
│   │  DISCIPLINES Registry (PAUD Focused + 11 Frameworks)   │    │
│   │  Canonical Theories · Pedagogical Rubrics (BB/MB/BSH)  │    │
│   └────────────────────────────┬───────────────────────────┘    │
│                                │                                │
│        ┌───────────────────────┼────────────────────────┐       │
│        ▼                       ▼                        ▼       │
│ ┌────────────────┐    ┌─────────────────┐      ┌──────────────┐ │
│ │ Google Gemini  │    │ SerpApi Engine  │      │ Zotero REST  │ │
│ │ 4-Model Chain  │    │ Google Scholar  │      │ Reference    │ │
│ │ Fallback Logic │    │ Live Index      │      │ Sync         │ │
│ └────────────────┘    └─────────────────┘      └──────────────┘ │
│                                                                 │
│ ┌─────────────────────────────┐   ┌───────────────────────────┐ │
│ │ Pure-JS Statistics Engine   │   │ RegEx Citation Validator  │ │
│ │ Paired t-Test · Hake N-Gain │   │ APA7 / CrossRef Checker   │ │
│ └─────────────────────────────┘   └───────────────────────────┘ │
│                                                                 │
│ ┌─────────────────────────────┐   ┌───────────────────────────┐ │
│ │ Persistent Document Memory  │   │ DOCX Academic Exporter    │ │
│ │ Local JSON Session Storage  │   │ Formatted Word Packaging  │ │
│ └─────────────────────────────┘   └───────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
             Deployed via Dockerfile → Railway Cloud

Core Code Snippet 1: Grounded Literature Retrieval with SerpApi

To ensure every factual claim has an inspectable source, server.js queries Google Scholar via SerpApi before composing the prompt:

// server.js (MIT Licensed)
async function searchGoogleScholar(query, limit = 6) {
  const serpApiKey = process.env.SERPAPI_API_KEY;
  if (!serpApiKey) {
    console.log('[Scholar] No SerpApi key provided, continuing without live search.');
    return [];
  }
  try {
    const url = `https://serpapi.com/search.json?engine=google_scholar&q=${encodeURIComponent(query)}&api_key=${serpApiKey}&hl=id&num=${limit}`;
    const response = await fetch(url);
    if (!response.ok) throw new Error(`SerpApi returned status ${response.status}`);
    const data = await response.json();

    return (data.organic_results || []).slice(0, limit).map((p, idx) => ({
      title: p.title || 'Untitled Academic Paper',
      authors: p.publication_info?.summary || 'N/A',
      link: p.link || '',
      snippet: p.snippet || '',
      citations: p.inline_links?.cited_by?.total || 0,
      year: (p.publication_info?.summary || '').match(/\b(20\d{2}|19\d{2})\b/)?.[1] || '2023'
    }));
  } catch (err) {
    console.error('[SerpApi Error]:', err.message);
    return [];
  }
}

Core Code Snippet 2: Transparent PTK Statistics Engine

The statistical engine runs directly in Node.js using pure mathematics, ensuring full visibility into every step of the calculation:

// server.js — Transparent calculation of Paired t-Test and Hake (1999) Normalized Gain
app.post('/api/stats/calculate', (req, res) => {
  const { pretest, posttest, maxScore = 100, variableName = 'Perkembangan Motorik Halus' } = req.body;
  const n = pretest.length;

  const diffs = pretest.map((pre, i) => posttest[i] - pre);
  const meanDiff = diffs.reduce((a, b) => a + b, 0) / n;
  const variance = diffs.reduce((sum, d) => sum + Math.pow(d - meanDiff, 2), 0) / (n - 1);
  const standardError = Math.sqrt(variance / n);
  const tStat = standardError === 0 ? 0 : meanDiff / standardError;
  const df = n - 1;

  const preAvg = pretest.reduce((a, b) => a + b, 0) / n;
  const postAvg = posttest.reduce((a, b) => a + b, 0) / n;
  const nGain = (maxScore - preAvg === 0) ? 0 : (postAvg - preAvg) / (maxScore - preAvg);
  const nGainCategory = nGain >= 0.7 ? 'Tinggi' : nGain >= 0.3 ? 'Sedang' : 'Rendah';

  res.json({
    success: true,
    tStat: tStat.toFixed(4),
    df,
    nGain: nGain.toFixed(4),
    nGainPercent: (nGain * 100).toFixed(2) + '%',
    nGainCategory,
    preAvg: preAvg.toFixed(2),
    postAvg: postAvg.toFixed(2),
    interpretation: generateAcademicProse({ variableName, tStat, df, nGain, nGainCategory, preAvg, postAvg })
  });
});

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How I Built It

The challenge explicitly accepts open-source agent harnesses as a qualifying open-source AI foundation. AcademAI is built around a custom, lightweight Node.js/Express open-source agent harness (server.js, MIT Licensed), powered by Google Gemini and grounded with SerpApi:

1. The Open-Source Agent Harness (server.js)

Instead of relying on heavy closed agent frameworks or bloated multi-container microservices, AcademAI is designed as an accessible, standalone open-source harness:

• Domain Knowledge Engine: Injects foundational theories (Piaget, Vygotsky, Montessori) and curriculum rubrics (STPPA, BB/MB/BSH/BSB) into the context based on user input.
• Pure JavaScript Math: Implements t-tests and Hake's N-gain without external proprietary statistical packages, ensuring transparency and repeatability.
• DOCX Word Exporter: Uses the open-source docx library to compile structured thesis drafts into standard .docx files for advisor review.
• Session Memory: Uses isolated session identifiers to persist research documents locally in ./data/memory.json.

2. Scholarly Grounding via SerpApi

• Generic LLMs invent citations because they predict tokens without index verification.
• We integrate SerpApi to query the live Google Scholar index, fetching real publication titles, snippets, citation counts, and direct links.
• These verified references are injected into the agent prompt, drastically reducing the risk of phantom citations.

3. AI Inference: Google Gemini with 4-Model Fallback Chain

We utilize Google Gemini for its state-of-the-art academic prose and generous free tier:

• If a model encounters a rate limit or server load during peak hours, the harness automatically cascades through fallback tiers (gemini-3.1-flash-lite → gemini-3.5-flash-lite → gemini-3.7-flash → gemini-3.8-flash) so the student's late-night writing session is never abruptly halted.

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Why Does Open Innovation Matter?

1. Removing Financial Barriers for Developing World Students

There are over 8 million university students across Indonesia. Most students in regional teacher-training colleges (LPTK) study on budget laptops or mobile devices.

• Commercial AI tools charge $20 to $30 per month — often equivalent to several weeks of a student's living budget in regional Indonesia.
• Open innovation — combining a lightweight MIT-licensed agent harness, accessible search APIs like SerpApi, and Gemini's free tier — proves that state-of-the-art research support can be made freely accessible without subscription gatekeeping.

2. Transparent, Defendable Academic Rigor

In a university thesis defence, a student cannot say: "the AI told me this was true." They must defend their sources, explain their data, and verify their citations.

• With open innovation, every algorithm in AcademAI is transparent.
• The student can inspect the exact mathematics of their t-test, click the direct link provided by SerpApi to verify a paper's existence, and inspect the prompt rules on GitHub. Open innovation preserves human agency and academic integrity.

3. Self-Hostable and Customizable

Because the application code is open-source (MIT), any university department or student union can clone the repository, customize the DISCIPLINES registry to match their local faculty guidelines, and host it independently on their own infrastructure.

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My Agent Session

AcademAI was engineered with the assistance of Antigravity IDE (Google DeepMind's agentic pair-programming system).

Throughout the session, the agent and I:

• Consolidated the prototype into a clean, standalone server.js agent harness to eliminate container bloat and ensure fast cold starts.
• Connected SerpApi for live Google Scholar index querying and grounded literature injection.
• Built and verified the pure JavaScript statistical engine for Classroom Action Research (PTK) and Hake's N-Gain formulas.
• Configured Google Gemini with an automated 4-model fallback cascade.
• Packaged the application with Docker and verified the live cloud deployment on Railway with automated /healthz monitoring.

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Prize Categories

• Best Use of SerpApi: AcademAI deeply integrates SerpApi to query the live Google Scholar index, grounding thesis writing in verified, peer-reviewed publications and providing students with traceable citation links.