Academic Title Recommender
A lightweight browser-based tool that helps students instantly generate academic paper title ideas based on their course/program and paper type. It removes the guesswork from brainstorming by delivering relevant, program-specific suggestions in seconds.
Core Objectives
Title Selection Flow
Paper Type Dropdown
The user chooses whether they are creating a thesis, capstone, or research paper.
Program Dropdown
The user selects a course such as BSCS, BSME, BSIT, or Nursing.
Form Validation
The app blocks submission until both required fields are selected.
Generate Button
A single action submits the form and starts the title lookup process.
Suggestion Results Flow
Template Lookup
The script retrieves a matching list of titles from a nested program-and-paper-type object.
Result Reveal
The output container becomes visible only after generation begins.
First Title Display
The app automatically shows the first title in the matched list.
Title Counter
A small counter shows which suggestion is currently being viewed.
Title Cycling Flow
Dice Button
Users can request another suggestion without regenerating the entire list.
Animated Roll State
The button briefly disables during the switch to mimic a rolling effect.
Sequential Rotation
The app advances through the stored title list one item at a time.
Persistent Session State
The current title list stays in memory until the user submits a new search.
Architecture & Processing Flow
1. User selects a paper type and academic program from the form.
2. The submit handler prevents the default form action and validates both inputs.
3. The UI disables the Generate button and reveals the results section.
4. generateSimulatedTitles() looks up a title array from a nested template object keyed by program and paper type.
5. The selected titles are stored in currentTitles, and currentTitleIndex is reset to the first suggestion.
6. displayCurrentTitle() renders the current suggestion, while the dice button advances through the remaining titles on demand.
The architecture is intentionally simple and fully client-side, which makes the app fast and easy to maintain. Instead of calling an API or storing records in a database, it relies on a structured in-memory object that maps each program to paper-type-specific title lists. That design keeps the app deterministic, low-latency, and easy to extend by adding more programs or new title arrays.
A small state model drives the user experience: currentTitles holds the active suggestion set, and currentTitleIndex tracks the current position in the list. This avoids regenerating data every time the user clicks the dice button and makes the “next title” interaction feel smooth. The code also uses progressive UI states, such as disabling controls during action and revealing results only when ready, which keeps the interface clear and prevents invalid interactions.
Project Impact
ACATIRE fundamentally accelerates the academic research pipeline by eliminating the notoriously restrictive brainstorming phase of thesis, capstone, and research projects. It acts as an instant catalyst for students, translating mere program selections into academically rigorous, context-aware title suggestions tailored directly to their field.
For educational institutions and student peers, the platform dramatically narrows the focus early in the research lifecycle. By cycling through varied, highly relevant suggestions with its dice-roll mechanics, students avoid generic topics and can quickly align on specialized ideas that meet institutional requirements.
Ultimately, ACATIRE empowers the next generation of researchers with immediate focus. By removing the friction of initial topic creation, it allows students to dedicate the vast majority of their energy toward the actual research, methodology, and impactful execution of their final comprehensive papers.