On this page
Learners import notes or chapters, then ask questions, get explanations grounded in their material, and practise with generated quizzes and flashcards. The design goal is understanding, not shortcuts.
Features and architecture
1. Ask with grounding (RAG)
Retrieve the most relevant chunks from the learner's notes, then answer using only them, with citations. See RAG on Android for indexing and retrieval details.
class AskUseCase(private val index: NotesIndex, private val llm: Llm) {
fun ask(question: String, mode: Mode): Flow<String> = flow {
val hits = index.search(question, topK = 5)
if (hits.isEmpty()) { emit("I couldn't find this in your notes. Try importing the relevant chapter."); return@flow }
val context = hits.joinToString("\n\n") { "[${it.id}] ${it.text}" }
val style = when (mode) {
Mode.EXPLAIN -> "Explain step by step for a student, then give one check-your-understanding question."
Mode.HINT -> "Give a hint only, not the full answer. Encourage the student to try."
}
emitAll(llm.stream(
"You are a study tutor. Use ONLY the notes below. Cite sources like [id]. " +
"If the notes don't contain the answer, say so.\n$style\n\nNotes:\n$context\n\nQuestion: $question"
))
}
enum class Mode { EXPLAIN, HINT }
}2. Generate quizzes as structured data
@Serializable
data class Question(val prompt: String, val options: List<String>, val correctIndex: Int, val explanation: String)
@Serializable
data class Quiz(val questions: List<Question>)
suspend fun generateQuiz(llm: Llm, chunk: String, count: Int = 5): List<Question> {
val raw = llm.generateJson(
"Create $count multiple-choice questions (4 options each) testing understanding of this text. " +
"Include a one-sentence explanation citing the text. Use only facts in the text.\n\n$chunk"
)
return Json { ignoreUnknownKeys = true }.decodeFromString<Quiz>(raw).questions
.filter { q -> q.options.size == 4 && q.correctIndex in 0..3 && q.options.toSet().size == 4 }
}Validate every question: model output can have duplicate options or out-of-range answers.
3. Flashcards with spaced repetition
@Entity data class Card(
@PrimaryKey val id: String,
val front: String, val back: String,
val intervalDays: Int = 0, val ease: Double = 2.5, val dueAt: Long = 0,
)
fun Card.review(grade: Int, now: Long): Card { // grade: 0 = forgot, 1 = hard, 2 = good, 3 = easy
val newEase = (ease + 0.1 - (3 - grade) * 0.08).coerceAtLeast(1.3)
val newInterval = when {
grade == 0 -> 1
intervalDays == 0 -> 1
intervalDays == 1 -> 3
else -> (intervalDays * newEase).roundToInt()
}
return copy(intervalDays = newInterval, ease = newEase, dueAt = now + newInterval * 86_400_000L)
}This is a simplified SM-2-style scheduler. Learners can edit generated cards before saving them.
Design for learning, not copying
- Offer Hint mode before full answers.
- End explanations with a check question.
- Show citations to the learner's notes, so they can verify.
- Track quiz results to suggest weak topics.
- Be clear that AI can be wrong. See Responsible AI UX.
Safety and privacy
- If your audience includes minors, follow applicable regulations and platform policies (for example Google Play's Families policies).
- Imported notes may be sent to a cloud model: disclose this and minimise what's sent (only retrieved chunks).
- Treat imported content as untrusted: it may contain instructions aimed at the model. See Prompt Injection.
Evaluate quality
Create a small test set of questions with known answers from sample notes, and check groundedness, correctness and refusal when information is missing. See Evaluating AI Responses.
Frequently asked questions
Can it work offline?
Flashcard review works offline. Q&A and generation need a model: cloud (online) or on-device (limited capability on supported devices).
Why not just use a general chatbot?
Grounding in the learner's material, quizzes and spaced repetition make it a learning tool rather than an answer machine.
Conclusion
A study assistant grounds answers in the learner's notes with citations, generates validated quizzes and flashcards with spaced repetition, and is designed to encourage understanding, with clear privacy and safety choices.
Next step: handle data responsibly in AI Privacy and Data Retention.
Official references
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