Key Takeaways
- Conventional authoring tools assume the author trained as an instructional designer, which is the production barrier faculty meet.
- Chapman Alliance measured 184 development hours for 1 finished hour of interactive eLearning in its 2010 benchmark study.
- An AI authoring tool reads the materials a course already has and returns a draft outline and draft quiz questions.
- Faculty review starts days after the upload, at the front of the production timeline.
- Version history records what the AI drafted, what the faculty member changed, and when the content was approved.
A faculty member can teach a subject for 90 minutes without preparing anything new. Turning those 90 minutes into an assessable online module that a learning management system can deliver is a separate job with its own toolchain and its own hours. That second job is where most workforce programs lose months.
Demand for the online version of that teaching is already settled. That demand is already visible in the enrollment data, because NCES reported 9.4 million undergraduates, 61% of them, taking at least 1 distance education course in fall 2021. Production capacity is the constraint those enrollment numbers put pressure on.
Why Faculty Hit a Production Wall in Conventional Authoring Tools
Most departments answer that capacity question with 1 person who already has a full teaching load. The authoring tools that person opens were built for instructional designers who work with learning objectives, branching logic and multimedia timelines every day. A faculty member opens that tool and meets a designer's job.
That assumption has a measured price. Chapman Alliance measured that price at 184 development hours for 1 finished hour of interactive eLearning, counted across the whole production team, in its 2010 benchmark study. Those hours are spread across people a department running a certificate program usually does not have.
The 2 Jobs a Conventional Tool Expects From 1 Person
A conventional tool asks the author to hold 2 roles at once. The first role is subject matter expertise, meaning what gets taught. The second is instructional design, which is knowing how to sequence that material so a learner can work through it and prove they understood it.
In a staffed L&D team those 2 roles belong to 2 people. The instructional designer in that team interviews the expert, pulls out what matters and builds the module around it. Most higher education departments have no such designer available for every workforce program they want to offer, so 1 person absorbs both roles.
What Lecture Materials Cost to Convert
Absorbing both roles is harder with lecture materials, because of what those materials were written for. Those materials support a speaker in a room, because slides are cues and notes are prompts for somebody who already knows the answer. Self-paced delivery needs 1 adaptation pass on each of those slide decks.
The adaptation pass is where the hours accumulate, and it repeats for every module the department builds. EDUCAUSE found in its 2024 Teaching and Learning Workforce report that 58% of teaching and learning professionals are responsible for faculty training and development, and 51% for instructional design. A team that small cannot absorb a conversion queue, so check what you are asking it to do:
- Count the modules each department wants to publish this year.
- Measure the hours 1 finished module takes from source file to LMS upload.
- Name the person who performs each conversion step today.
Turn the course you already teach into a module this month.
KC Studio drafts the outline and the quiz questions from your own materials, and leaves the subject judgment with you.
How AI Authoring Tools Read the Materials Faculty Already Have
An AI authoring tool starts that same conversion from the other end. The faculty member uploads what already exists, which is a document, a slide deck or a recorded session, and the tool reads it. The tool takes the structure out of what you uploaded, so step 1 becomes reading an outline that already exists.
Everything it returns at that point is 1 draft and nothing more. It segments the content into teachable units, proposes an outline and writes quiz questions from the lesson material, which is the work that used to begin on a blank canvas. Every one of those outputs still needs a subject expert's check.
What the Tool Drafts, and What You Still Decide
The draft covers the work that used to need an instructional designer in the room. You decide what the draft cannot settle, which is whether the content is accurate, whether the terminology matches your field and whether the questions test what you meant to test. That division is what makes the output usable in an accredited program.
The question draft is the clearest case of that division. The tool reads the lesson content and writes the questions, which removes the blank-page hours and leaves your judgment about a fair question in your discipline untouched. You review each question, regenerate the 2 or 3 that miss, and keep the set that matches the material.
Production step | Conventional authoring tool | AI-assisted authoring tool |
|---|---|---|
Module outline | Designer builds it by hand after reading the source materials | Tool drafts it from the uploaded document or recording |
Assessment items | Faculty write questions, designer formats and codes each one | Tool drafts questions from lesson content, faculty review and regenerate |
Review starts | After the full module is built, often months later | From the first draft, days after upload |
Approval record | Tracked in whatever system the department uses | Tracked as versions beside the module itself |
Where Faculty Review Moves in the Production Timeline
Review used to wait at the back of that production queue. A department that waits 3 months for a designer to build a module waits the same 3 months before anybody with subject authority can read it. Those 3 months are 2 more courses taught by the faculty member.
An AI draft arrives days after the upload, which moves review to the front of the queue. The faculty member reads that draft while the material is still the one they just taught. Corrections to that draft cost minutes now, and the same corrections 3 months later cost a rebuild.
Keeping the Approval Record
Those corrections are also the record your program needs. Version history turns those corrections into a record, showing what the tool drafted, what the faculty member changed and when they approved it. For a continuing education program under accreditation review, that history is the evidence that a subject matter expert signed the content off.
Keep the record in the same system where the module lives. 1 instructional designer supporting 10 departments can show oversight across all of them when every approval is stamped in that same system. Their work becomes reviewing drafts, and the record follows each module.
What KC Studio Changes for Faculty Course Development
KC Studio is KnowledgeCity's AI course-authoring tool, and it drafts a course from a prompt, a document or a transcript. It writes quiz question drafts from the lesson content, and the faculty member reviews each one, regenerates the questions that miss and keeps the set that fits. Courses built there publish into the KC LMS catalog, so assignment, delivery and completion records stay where your other courses already are.
For higher education institutions, AI changes faculty course content development from a production project into a review task. A department running workforce programs in higher education uploads what it already teaches, reads the draft that comes back and corrects it. The hours that used to separate a faculty member's expertise from a learner's access to it are the hours the tool absorbs, and your department keeps the judgment.
Start with 1 course that already has materials and a waiting audience. The route from lecture notes to a published module has 3 steps:
- Upload the materials for 1 course you already teach.
- Read the draft against what you say in the room.
- Correct the content, approve it, and publish the module.
Frequently Asked Questions
1. What is the difference between an eLearning authoring tool and an LMS?
An authoring tool is where course content is built, and a learning management system is where that content is assigned, delivered and tracked. The authoring tool produces the module, and the LMS records who completed it. Content built in 1 is published or exported into the other, which is why institutions usually run both.
2. How well does AI handle highly technical subject matter?
An AI authoring tool drafts structure from the source material you give it, and it does not verify specialist content. For technical disciplines, the faculty review step is where accuracy is established. The tool produces the outline, the segments and a first set of questions, and the subject matter expert confirms the terminology, the depth and the competency level each question tests.
3. What source formats can faculty upload?
AI authoring tools generally accept documents, PDFs, slide decks and recorded video or audio. KC Studio drafts a course from a prompt, a document or a transcript, so the materials a faculty member already keeps for teaching are usually enough to start. Quiz generation reads the lesson content in the course, so a module with lessons in place produces a fuller question draft.
4. Does an AI-generated module skip faculty approval?
No. The AI draft enters the same curriculum review a new module would face, and it is a draft until a faculty member approves it. The practical change is when that review happens, because the draft exists days after upload. Faculty edit a structured draft, which takes less time than authoring the same module from a blank canvas.
5. How does this help a department with no instructional designer?
The tool performs the steps a designer would otherwise perform, which are segmenting content, proposing an outline and drafting assessment items. The department supplies the subject knowledge and the approval. Where a shared designer does exist, their time moves from building modules to reviewing them, which is how 1 designer covers 10 departments.
References
- Chapman Alliance. How Long Does It Take to Create Learning? 2010. slideshare.net
- EDUCAUSE. The Teaching and Learning Workforce in Higher Education, 2024. library.educause.edu
- National Center for Education Statistics. Fast Facts: Distance Learning. 2023. nces.ed.gov
- EDUCAUSE. 2025 EDUCAUSE Horizon Report: Teaching and Learning Edition. 2025. library.educause.edu
- EDUCAUSE Review. Augmented Course Design: Using AI to Boost Efficiency and Expand Capacity. 2024. er.educause.edu