I would like to suggest an enhancement to the AI teleprompter that would make it much easier to follow and deliver AI-generated answers naturally during a live interview.
When an interview is happening in real time, the AI may generate a long response. The biggest challenge is not only scrolling through the response but also knowing what part of the answer I should read, where one thought ends and the next thought begins, what the actual interview question was, and what tone I should use when delivering the answer.
1. AI Teleprompter with Auto-Scroll
The teleprompter should automatically follow newly generated AI responses without requiring manual scrolling.
Ideally:
Automatically scroll as new content is generated
Keep the current sentence/line near the center of the screen
Highlight the exact sentence currently being read
Automatically move to the next sentence
Provide adjustable scrolling speed
Include pause/resume controls
Support voice tracking when possible
If voice tracking or automatic scrolling is not reliable, the system should still provide another simple way to move from one sentence to the next without requiring the user to manually scroll with a mouse or touchpad.
2. Clearly Identify the Actual Interview Question
Sometimes the AI generates an answer without making it immediately obvious what interview question the answer is responding to.
It would be extremely helpful if the teleprompter clearly separated:
INTERVIEWER QUESTION
Tell me about a challenging data engineering project you worked on.
AI RESPONSE
I recently worked on a healthcare data pipeline where...
This would allow the user to quickly understand the question before reading the answer.
If possible, the system could automatically detect the question from the interviewer's audio and display it at the top of the teleprompter.
3. Sentence and Thought Grouping
Another useful feature would be to visually divide the answer into connected thought groups.
For example:
Point 1: Situation
In my current role, I work with large healthcare datasets...
Point 2: Challenge
One of the main challenges was handling inconsistent data...
Point 3: Solution
I addressed this by building a PySpark-based transformation pipeline...
Point 4: Result
This reduced processing time and improved data quality...
The user should be able to immediately see where a connected thought starts and where it ends.
This would be especially useful for longer AI-generated responses.
4. Tone Guidance for Each Section
I would also like to see an optional tone indicator for different parts of the response.
For example:
🟢 Friendly / Conversational
I really enjoyed working on this project because...
🔵 Professional / Confident
My responsibility was to design and implement...
🟡 Emphasize This Point
The biggest improvement was reducing the pipeline runtime by 40%.
🟣 Technical Explanation
We used PySpark to distribute the transformation workload...
This would help the user understand how the sentence should be delivered, rather than having to think about tone while simultaneously listening to the recruiter and reading the answer.
5. Detect the Recruiter's Tone
An even more advanced option would be for the AI to analyze the recruiter's question and suggest an appropriate response tone.
For example:
Recruiter:
"Can you tell me about a time when you had a disagreement with your manager?"
AI guidance:
Tone: Friendly + Reflective
If the recruiter asks a highly technical question:
Tone: Confident + Technical
If the recruiter asks a casual conversational question:
Tone: Friendly + Conversational
If the recruiter asks a behavioral question:
Tone: Professional + Storytelling
The system could show a small tone indicator rather than requiring the user to think about this during the interview.
6. Visual Connection Between Related Sentences
It would also be helpful if connected sentences were visually grouped so the user knows which lines belong together.
For example:
▶ Situation I was working on a healthcare data pipeline... ↓ ▶ Challenge The existing process was taking several hours... ↓ ▶ Action I redesigned the pipeline using PySpark... ↓ ▶ Result The processing time was reduced significantly...This makes it much easier to understand the structure of the answer at a glance.
7. Simple Reading Mode
The final teleprompter view should ideally be very clean:
──────────────────────────────────────── QUESTION Tell me about a challenging project. Tone: Friendly + Confident ──────────────────────────────────────── CURRENT POINT ▶ I recently worked on a healthcare data pipeline where we had to... ──────────────────────────────────────── Next: The biggest challenge was... ────────────────────────────────────────The goal is to reduce the amount of mental effort required during the interview.
Instead of thinking about:
What was the question?
Where should I start reading?
Where does this thought end?
What should I emphasize?
Should I sound formal or friendly?
Do I need to scroll?
What comes next?
The system would provide those cues automatically.
Overall Request
I think combining AI Teleprompter + Auto-Scroll + Question Detection + Sentence Grouping + Tone Guidance + Voice Tracking would make the interview experience much easier and more natural.
Even if voice tracking or auto-scroll cannot reliably follow the user's voice, having clear sentence boundaries, connected thought groups, question identification, and tone indicators would still make the feature extremely useful.
This would turn the generated AI response from a block of text into a structured, easy-to-follow interview conversation guide.