The clearest way to understand Jason Dom/APUS’s approach is as a “guided AI” model: AI is treated as a legitimate part of higher education, but its use should be transparent, course-specific, governed by faculty/institutional rules, and designed to support learning rather than substitute for the student’s own work.
There is an important distinction, though: Jason Dom’s own public comments are limited. His ibl.ai testimonial specifically emphasizes instructor control and governance. The broader picture comes from APUS’s current policies and guidance and from ibl.ai’s description of the platform.
1. The central idea: don’t simply ban AI
APUS’s published guidance explicitly says the university embraces generative AI as a learning tool and encourages students to understand both its benefits and risks. The university’s current policy permits or encourages AI use in some courses and assignments when the instructor or assignment directions say so.
That produces a middle position between:
- “AI is cheating and should be prohibited.”
- “Students can use AI to do whatever they want.”
The APUS model is closer to:
AI can be part of the educational process, but the student remains responsible for demonstrating learning and following the rules of the particular course.
APUS’s September 2026 student guidance makes the philosophy particularly explicit: students should use AI “to learn—not to avoid learning.”
2. What Jason Dom adds to that philosophy
Jason Dom’s ibl.ai testimonial is short but revealing:
“ibl.ai gives instructors far more control than ChatGPT… I can decide what it won’t answer, define the personality, and point students to our own campus resources.”
His other quoted statement describes the platform as giving APUS flexibility to deploy multiple AI models while keeping governance “front and center.”
Those comments suggest three priorities.
A. Faculty control
The professor—not the AI—remains responsible for determining how AI fits into the course.
That matters because a generic ChatGPT interaction doesn’t necessarily know:
- the course’s learning objectives;
- what the professor has taught;
- which sources are authoritative;
- what students are expected to know independently;
- what constitutes acceptable assistance;
- which questions should not be answered.
ibl.ai’s current Faculty Agent explicitly describes its role as supporting instructors while keeping the instructor “firmly in control of all academic and pedagogical decisions.”
B. Guardrails rather than unrestricted generation
Dom’s phrase “I can decide what it won’t answer” is particularly significant.
The idea is that an educational AI can be configured to say, in effect:
“I can help you understand this concept, but I won’t write the answer to your graded assignment.”
That is substantially different from giving students an unrestricted general-purpose chatbot.
C. Institutional context
Dom also mentions directing students to campus resources.
That means the AI isn’t necessarily intended to be an independent authority. It can be connected to the institution’s own:
- policies;
- course materials;
- student services;
- academic resources;
- faculty guidance.
ibl.ai describes this architecture as allowing institutions to connect AI agents to systems such as LMSs and student-information systems while retaining control of data and deployment.
3. What this means for students
The APUS approach effectively creates three different categories of AI use.
Permitted learning assistance
APUS explicitly identifies uses such as:
- brainstorming;
- research assistance;
- explanations of difficult concepts;
- study guides;
- flashcards;
- summaries;
- editing assistance, subject to course rules.
For example, a student could ask AI:
“Explain the difference between correlation and causation as if I’m encountering statistics for the first time.”
That is fundamentally different from:
“Write my 1,500-word statistics paper.”
The first supports learning. The second can replace the student’s work.
AI-assisted work requiring disclosure
APUS says AI-generated material that appears in a submission must be appropriately cited or attributed, and students may be required to explain how and to what extent they used AI.
That creates a principle of AI transparency.
The question isn’t simply:
“Did you use AI?”
It becomes:
“What did you use it for, and what intellectual work did you personally perform?”
Prohibited substitution
APUS currently identifies as inappropriate, among other things:
- having GenAI write an assignment or discussion post;
- failing to verify AI-generated information;
- failing to provide appropriate attribution;
- using AI during tests/exams/quizzes without permission.
And APUS’s academic-misconduct provisions specifically identify inappropriate AI use as academic misconduct.
So the student’s fundamental obligation remains:
The work submitted for academic credit must actually demonstrate the student’s learning.
4. What this means for faculty
This model puts considerably more responsibility on instructors.
Faculty aren’t simply being asked to detect AI-generated papers. They’re being encouraged to design the learning environment around AI’s existence.
That is a major conceptual shift.
ibl.ai’s Faculty Agent, for example, is designed to help faculty:
- develop syllabi;
- create assignments;
- develop rubrics;
- generate examination questions;
- prepare lectures;
- assist with grading and feedback;
- identify assignments that are easily completed by AI;
- redesign those assignments when appropriate.
But the system explicitly positions AI outputs as drafts requiring faculty review, rather than autonomous academic decisions.
That is important.
The professor remains the decision-maker.
AI might say:
“Here are five possible rubric criteria.”
The professor decides which criteria actually belong in the course.
AI might flag:
“This assignment could be answered effectively by a language model.”
The professor decides whether and how to redesign it.
AI might generate feedback based on a rubric.
The instructor retains responsibility for the final judgment.
That is consistent with the governance emphasis in Dom’s comments.
5. The biggest implication for academic integrity
This is probably the most interesting aspect of the APUS/ibl.ai model.
Traditional academic-integrity systems largely ask:
“Did the student cheat?”
AI forces universities to ask a more complicated question:
“What constitutes authentic student work when AI is an ordinary part of the student’s intellectual environment?”
APUS’s answer is not that every use of AI is cheating.
Instead, it distinguishes between assistance and substitution.
The clearest way to understand Jason Dom/APUS’s approach is as a “guided AI” model: AI is treated as a legitimate part of higher education, but its use should be transparent, course-specific, governed by faculty/institutional rules, and designed to support learning rather than substitute for the student’s own work.
There is an important distinction, though: Jason Dom’s own public comments are limited. His ibl.ai testimonial specifically emphasizes instructor control and governance. The broader picture comes from APUS’s current policies and guidance and from ibl.ai’s description of the platform.
1. The central idea: don’t simply ban AI
APUS’s published guidance explicitly says the university embraces generative AI as a learning tool and encourages students to understand both its benefits and risks. The university’s current policy permits or encourages AI use in some courses and assignments when the instructor or assignment directions say so.
That produces a middle position between:
- “AI is cheating and should be prohibited.”
- “Students can use AI to do whatever they want.”
The APUS model is closer to:
AI can be part of the educational process, but the student remains responsible for demonstrating learning and following the rules of the particular course.
APUS’s September 2026 student guidance makes the philosophy particularly explicit: students should use AI “to learn—not to avoid learning.”
2. What Jason Dom adds to that philosophy
Jason Dom’s ibl.ai testimonial is short but revealing:
“ibl.ai gives instructors far more control than ChatGPT… I can decide what it won’t answer, define the personality, and point students to our own campus resources.”
His other quoted statement describes the platform as giving APUS flexibility to deploy multiple AI models while keeping governance “front and center.”
Those comments suggest three priorities.
A. Faculty control
The professor—not the AI—remains responsible for determining how AI fits into the course.
That matters because a generic ChatGPT interaction doesn’t necessarily know:
- the course’s learning objectives;
- what the professor has taught;
- which sources are authoritative;
- what students are expected to know independently;
- what constitutes acceptable assistance;
- which questions should not be answered.
ibl.ai’s current Faculty Agent explicitly describes its role as supporting instructors while keeping the instructor “firmly in control of all academic and pedagogical decisions.”
B. Guardrails rather than unrestricted generation
Dom’s phrase “I can decide what it won’t answer” is particularly significant.
The idea is that an educational AI can be configured to say, in effect:
“I can help you understand this concept, but I won’t write the answer to your graded assignment.”
That is substantially different from giving students an unrestricted general-purpose chatbot.
C. Institutional context
Dom also mentions directing students to campus resources.
That means the AI isn’t necessarily intended to be an independent authority. It can be connected to the institution’s own:
- policies;
- course materials;
- student services;
- academic resources;
- faculty guidance.
ibl.ai describes this architecture as allowing institutions to connect AI agents to systems such as LMSs and student-information systems while retaining control of data and deployment.
3. What this means for students
The APUS approach effectively creates three different categories of AI use.
Permitted learning assistance
APUS explicitly identifies uses such as:
- brainstorming;
- research assistance;
- explanations of difficult concepts;
- study guides;
- flashcards;
- summaries;
- editing assistance, subject to course rules.
For example, a student could ask AI:
“Explain the difference between correlation and causation as if I’m encountering statistics for the first time.”
That is fundamentally different from:
“Write my 1,500-word statistics paper.”
The first supports learning. The second can replace the student’s work.
AI-assisted work requiring disclosure
APUS says AI-generated material that appears in a submission must be appropriately cited or attributed, and students may be required to explain how and to what extent they used AI.
That creates a principle of AI transparency.
The question isn’t simply:
“Did you use AI?”
It becomes:
“What did you use it for, and what intellectual work did you personally perform?”
Prohibited substitution
APUS currently identifies as inappropriate, among other things:
- having GenAI write an assignment or discussion post;
- failing to verify AI-generated information;
- failing to provide appropriate attribution;
- using AI during tests/exams/quizzes without permission.
And APUS’s academic-misconduct provisions specifically identify inappropriate AI use as academic misconduct.
So the student’s fundamental obligation remains:
The work submitted for academic credit must actually demonstrate the student’s learning.
4. What this means for faculty
This model puts considerably more responsibility on instructors.
Faculty aren’t simply being asked to detect AI-generated papers. They’re being encouraged to design the learning environment around AI’s existence.
That is a major conceptual shift.
ibl.ai’s Faculty Agent, for example, is designed to help faculty:
- develop syllabi;
- create assignments;
- develop rubrics;
- generate examination questions;
- prepare lectures;
- assist with grading and feedback;
- identify assignments that are easily completed by AI;
- redesign those assignments when appropriate.
But the system explicitly positions AI outputs as drafts requiring faculty review, rather than autonomous academic decisions.
That is important.
The professor remains the decision-maker.
AI might say:
“Here are five possible rubric criteria.”
The professor decides which criteria actually belong in the course.
AI might flag:
“This assignment could be answered effectively by a language model.”
The professor decides whether and how to redesign it.
AI might generate feedback based on a rubric.
The instructor retains responsibility for the final judgment.
That is consistent with the governance emphasis in Dom’s comments.
5. The biggest implication for academic integrity
This is probably the most interesting aspect of the APUS/ibl.ai model.
Traditional academic-integrity systems largely ask:
“Did the student cheat?”
AI forces universities to ask a more complicated question:
“What constitutes authentic student work when AI is an ordinary part of the student’s intellectual environment?”
APUS’s answer is not that every use of AI is cheating.
Instead, it distinguishes between assistance and substitution.
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