AI-Powered · Registrar Tool
Free AI Course Scheduling Conflict Detector
Paste or upload your draft timetable. AI scans for room double-bookings, instructor clashes, and student cohort overlaps — then suggests conflict-free alternatives.
AI-generated output · Free account required · Results may vary
Term Configuration
Schedule Data
Required columns (comma or tab-separated, with header row):
course_code, room, day, start_time, end_time, instructor Optional: student_groups (pipe-separated: CS-2023|ENG-2023) Detection Settings
~-- credits · ~-- tokens estimated — updates with entry count
Paste or upload a schedule to enable detection
How to Use This Tool
Follow these steps to get results in under a minute
01
Enter term details
Provide the semester name and institution so the AI contextualises its suggestions.
02
Paste or upload your draft schedule
Use the paste tab for quick input or upload a CSV. Download our sample template if you need a starting point.
03
Choose conflict types to scan
Select room conflicts, instructor clashes, and/or student cohort overlaps. Set your minimum buffer time.
04
Generate and export the conflict report
Review hard and soft conflicts with AI-suggested fixes. Export to CSV or PDF for your planning team.
Common Questions
Frequently Asked Questions
What types of scheduling conflicts does this tool detect?
The tool detects three categories: hard conflicts (room double-bookings, instructor clashes — two classes assigned the same room or instructor at the same time) and soft conflicts (student cohort overlaps — students enrolled in two simultaneous courses). You can configure which types to scan.
What format should I paste or upload my timetable in?
The tool accepts CSV with six required columns: course_code, room, day, start_time (HH:MM), end_time (HH:MM), and instructor. An optional student_groups column (pipe-separated for multiple groups, e.g. CS-2023|ENG-2023) enables cohort overlap detection. Download the sample template to get started quickly.
How many schedule entries can I analyse in one run?
The tool processes up to 75 schedule entries per generation. For large timetables, split by faculty or department and run separately. The credit cost scales with entry count.
Is this tool free to use?
Free accounts include 100 AI credits on sign-up. Each generation costs 12 base credits plus 2 per schedule entry. A 10-entry timetable costs ~32 credits — around 3 free runs.
Can I export the conflict report?
Yes. After generation, download a CSV conflict report (Excel-compatible with UTF-8 BOM) or print a formatted PDF scorecard for your academic planning committee.
How AI Course Scheduling Conflict Detector Compares
vs spreadsheets, manual processes, and paid platforms
| Feature | UniCloud360 AI Course Scheduling Conflict Detector | Manual cross-check | Spreadsheet formula | Basic room booking tool |
|---|---|---|---|---|
| Room double-booking detection | Scans all entries and flags exact room-time conflicts instantly. | Requires manually comparing each row. | Possible with complex formulas but error-prone. | Checks rooms only, not instructors or cohorts. |
| Instructor clash detection | Identifies when the same instructor appears in two simultaneous slots. | Requires manual scan across staff columns. | Difficult formula to set up and maintain. | Typically not supported. |
| Student cohort overlap detection | Flags enrolled student groups appearing in two simultaneous courses. | Not possible without group data. | Not practical to implement. | Not supported. |
| AI-suggested conflict-free alternatives | Returns specific room, time, or instructor swap suggestions per conflict. | No suggestions generated. | No suggestions provided. | May show open slots but without AI reasoning. |
| CSV and PDF export | Exports Excel-compatible CSV conflict report and PDF scorecard. | Copy and paste only. | Native spreadsheet export. | Limited export formats. |
Real Results from Real Users
Trusted by lecturers and students across Sri Lankan universities
4.9
★★★★★
3 reviews
PF
"Found 11 conflicts in our draft timetable in under 30 seconds. The room double-bookings would have caused chaos on day one of semester."
DA
"The alternative suggestions were spot on. We accepted 8 of the 10 recommendations without any modification."
CW
"Saved our team two days of manual cross-checking. Now we run this before every semester draft goes to faculty."
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