Note: This is a composite case study based on typical implementation patterns and aggregate data from early Aprivo adopters. University details are illustrative, but the numbers reflect real-world results.
The Challenge: 3,000 Decks, 4 Months, and a Federal Deadline
In late 2025, the disability services director at a mid-size public university in the Midwest sat down with a spreadsheet and a growing sense of dread.
The university enrolled roughly 15,000 students across 12 academic departments. Like most institutions, it had been using Blackboard Ally for several years to scan course content and generate accessibility scores. Ally had done its job well: it surfaced exactly how big the problem was. Across the LMS, more than 3,000 PowerPoint presentations were flagged with accessibility scores below 50%.
The ADA Title II compliance deadline — April 2026 — was four months away. The Office for Civil Rights had made it clear that digital content accessibility was an enforcement priority. The university's legal counsel had flagged the risk. The provost wanted a plan.
The current approach wasn't going to cut it.
The Existing Remediation Workflow
The disability services office had four student workers trained in basic accessibility remediation. Each worker earned $15/hour and could process roughly 5 PowerPoint decks per shift, spending an average of 90 minutes per deck on:
- Writing alt text for images (opening each image, interpreting the content, composing a description)
- Checking and correcting reading order via PowerPoint's Selection Pane
- Verifying slide titles and adding missing ones
- Running the built-in Accessibility Checker and addressing flagged items
At 5 decks per day across four workers, the team processed about 20 decks per day. At that rate, clearing the 3,000-deck backlog would take 150 working days, or roughly 7.5 months. The deadline was in 4.
The math didn't work. Hiring more student workers was the obvious answer, but the budget for the current team was already stretched. Doubling the staff would cost an estimated $72,000 in wages over the remediation period, and finding students with the right training and attention to detail was its own challenge.
The Evaluation: Scanning Tools vs. Remediation Tools
The accessibility coordinator evaluated four options:
Blackboard Ally (already deployed). Ally was already scanning files and generating scores, but it didn't modify the source PowerPoint files. The alternative formats it generated (HTML, audio) helped students, but they didn't satisfy the requirement to make the original materials accessible. Ally identified the problem; it couldn't fix it.
Pope Tech. Another scanning and reporting platform. Excellent for website accessibility auditing, but like Ally, it's a diagnostic tool for documents. It can identify PPTX issues but doesn't remediate them.
Hiring additional student workers. Effective but expensive and slow to ramp up. Training new workers took 2-3 weeks before they were producing consistent quality. The budget increase required approval the office wasn't confident it would get in time.
Aprivo. An AI-powered tool purpose-built for PowerPoint accessibility remediation. Unlike the scanning tools, Aprivo modifies the source PPTX: generating alt text, correcting reading order, setting language tags, and verifying slide titles. The output is a remediated file, not a report.
The Pilot: 50 Decks From the Education Department
Before committing, the team ran a controlled pilot. They selected 50 PowerPoint decks from the College of Education — a mix of lecture slides, student-facing handouts, and image-heavy presentations used in teaching methods courses.
Pilot Setup
- Uploaded all 50 decks to Aprivo over two days
- One staff member reviewed AI-generated alt text and suggested fixes for each deck
- Compared output quality against 10 decks that had been manually remediated by the student team
Pilot Results
| Metric | Manual Process | Aprivo |
|---|---|---|
| Average time per deck | 90 minutes | 12 minutes |
| Alt text quality rated "good" or "excellent" | 78% (student workers) | 87% (AI-generated) |
| Reading order corrections needed post-process | 15% of slides | 4% of slides |
| Decks processed per staff member per day | 5 | 35-40 |
The time reduction was the headline number: from 90 minutes to 12 minutes per deck, an 87% reduction. But the quality metrics mattered just as much. The AI-generated alt text was rated equal to or better than the student-written descriptions in the majority of cases, particularly for charts, diagrams, and complex images where contextual understanding made a difference.
The 12-minute figure included human review time. The accessibility coordinator emphasized that Aprivo didn't remove the human from the process — it changed their role from writing descriptions and hunting for issues to reviewing AI suggestions and approving fixes. That's a fundamentally different (and faster) task.
The Rollout: 3 Months to Clear the Backlog
Based on the pilot results, the university subscribed to Aprivo's Max plan at $199/month and reassigned its remediation workflow.
New Process
- Department liaison identifies priority decks — active course materials first, followed by frequently reused content.
- Batch upload to Aprivo — staff uploads decks in groups of 20-30.
- AI remediation runs — Aprivo processes each deck, generating alt text, correcting reading order, verifying titles, and setting language tags.
- Staff review — one trained reviewer checks AI suggestions, approves or edits alt text, and flags any edge cases for manual attention.
- Download and re-upload — remediated PPTX files go back into the LMS, where Ally automatically rescans and updates scores.
The four student workers, who previously spent their shifts on manual remediation, were reassigned to the review step. Because reviewing is faster than writing from scratch, each worker could now handle 35-40 decks per day instead of 5.
Results After 3 Months
| Metric | Before | After |
|---|---|---|
| Decks remediated | 312 (over previous 4 months) | 2,400 (over 3 months) |
| Average Ally score (remediated decks) | 42% | 89% |
| Remediation cost (labor) | ~$18/deck | ~$2.50/deck |
| Monthly tool cost | $0 | $199 |
| Total estimated cost for 2,400 decks | $43,200 (labor only) | $6,597 ($199 x 3 months + labor) |
| Staff overtime hours | 120+ hours | 0 hours |
| Departments fully compliant | 2 of 12 | 9 of 12 |
The remaining 600 decks — spread across three departments with unusually complex content (STEM diagrams, medical imaging, fine arts photography) — were in progress and on track for completion before the April 2026 deadline.
What the Team Learned
The accessibility coordinator shared several observations from the implementation:
AI alt text was strongest for charts and data visualizations. Aprivo's use of Claude's vision AI meant it could read axis labels, identify trends, and describe data relationships. These were the descriptions that took student workers the longest to write manually and where AI quality was highest.
Human review still matters. About 13% of AI-generated alt text needed editing — typically for highly specialized disciplinary content (e.g., a geology cross-section diagram where the AI described the visual accurately but used the wrong geological terminology). The review step caught these cases.
Faculty engagement improved. When faculty saw how quickly their decks could be remediated, several began uploading new materials proactively rather than waiting for the central office to process them. The free tier (10 decks/month) was enough for most individual instructors.
Ally scores aren't everything, but they drive behavior. Watching departmental Ally scores climb from red to green created visible momentum. Deans and department chairs who had been passive about accessibility started asking how to get their remaining materials processed.
The Bottom Line
The university's accessibility backlog wasn't a technology problem or an awareness problem. The tools to identify issues (Ally, PowerPoint's checker) were already in place. The gap was in fixing those issues at scale without an unrealistic budget or timeline.
Aprivo didn't replace the accessibility team. It changed the economics of their work — turning a $72,000+ manual remediation project into a $6,600 process that finished ahead of deadline.
For institutions facing similar backlogs with the April 2026 ADA Title II deadline ahead, the calculus is straightforward: the cost of automated remediation is a fraction of the manual alternative, and the timeline compresses from months to weeks.
See what Aprivo can do for your institution. Start with 10 free decks.
Frequently Asked Questions
What size institution does Aprivo make sense for?
Aprivo scales across institution sizes. Individual faculty can use the free tier (10 decks/month) or Basic plan ($9/month) for their own course materials. Departments or small colleges may find the Pro plan ($29/month) sufficient. The Max plan ($199/month) is designed for institutional-level remediation — disability services offices, instructional design teams, or central IT groups processing materials across departments.
How does AI-generated alt text handle discipline-specific content?
The AI performs well with most academic content, including charts, photographs, diagrams, and screenshots. For highly specialized imagery (medical scans, advanced scientific visualizations, fine art), the AI provides a strong starting description that a subject-matter expert can refine during the review step. The goal is to handle the 85-90% of straightforward cases automatically so human expertise can focus on the edge cases.
Can we track progress across departments?
Aprivo provides processing history and metrics for your account. Institutions using the Max plan can track volume across their remediation queue. Combined with Ally's institutional reporting, you get a complete picture: Ally shows which files still need attention, and Aprivo's logs show what's been processed and when.