This article will give you tips on how to analyze responses from an online course student survey about technical support using AI-powered tools and smart workflows.
Choosing the right tools for analysis
The tools and approach you'll use depend a lot on your data type—whether you're working with numbers or open-ended feedback.
Quantitative data: For simple stats, like counting how many students selected each technical support option, classic tools like Excel or Google Sheets do the trick fast and efficiently.
Qualitative data: For responses to open-ended questions or rich follow-up feedback, things get tricky. These insights are impossible to manually read and summarize at scale. This is where AI tools really shine and save a lot of time and effort.
When working with qualitative responses, there are two approaches for tooling:
ChatGPT or similar GPT tool for AI analysis
Copy-paste your exported data into ChatGPT or a similar tool, and start chatting about it.
This is convenient for quick analysis if you don't have a lot of data, but things can get messy. Formatting issues, context size limits, and manually keeping track of threads all slow you down. It's not ideal if your data set is large or if you regularly run surveys like this.
All-in-one tool like Specific
Specific is a purpose-built AI platform for survey analysis—it both collects and analyzes data.
When you run a survey with Specific, the AI agent asks followup questions in real time. This results in much deeper, clearer data than you'd get from a standard form. It then summarizes responses, finds key themes, and delivers instant insights—there's no manual spreadsheet wrangling or stitching together results. You can also chat live with the AI about your results, controlling the context you send over for in-depth breakdowns.
Check out details on AI survey response analysis features if you want an all-in-one workflow for analyzing survey responses using AI.
If you're looking to create your own survey for this use case: you can start fast with the survey generator specifically for technical support surveys with online course students or explore the general AI survey builder for any custom scenario.
Useful prompts that you can use for survey analysis of online course student feedback on technical support
Prompts are your superpower when chatting with AI to analyze survey trends, pain points, and sentiments. Here’s what works best for this specific audience and topic:
Prompt for core ideas: Use this to distill the big themes from any set of qualitative answers.
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
The AI always gives better results if you provide more background. For example, tell the AI about your survey's purpose or anything relevant about your online course students. Here’s how you can deliver this context:
This survey was run with online course students to gather detailed feedback about problems and quality of technical support. Our ultimate goal is to identify areas needing improvement and what students really expect. Focus your summary on key themes and pain points most relevant for technical support experiences in online learning.
Dive deeper into a topic: Once you have your main themes, ask follow-up questions by referencing the core idea directly.
Tell me more about XYZ (core idea)
Prompt for specific topic: If you want to see if anyone mentioned something ("quick response time," "FAQs," "24-hour support," etc.):
Did anyone talk about [specific topic]? Include quotes.
Prompt for personas: This prompt helps you spot recurring user types among your online course students:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: Essential for identifying what’s broken in your technical support journey:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for motivations & drivers: Helpful for learning why students care about particular support features:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for sentiment analysis: Is the mood mostly positive or do learners feel let down?
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for suggestions & ideas: Find direct recommendations from students about technical support:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: Spot gaps and ideas for improvement:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
How Specific analyzes qualitative data from different question types
The strength of Specific is its flexibility for any question format you use during your technical support survey:
Open-ended questions (with or without followups): Specific gives you a summary for all student responses, and for any additional followup feedback collected related to the main question. This helps you spot the biggest themes fast. For strategies on writing powerful open-ended questions, check out best questions for technical support surveys.
Single- or multi-choice questions with followups: For each option students choose, Specific provides a targeted summary of related followup comments. This puts each answer in context, letting you see not just what students thought but why.
NPS (Net Promoter Score): Specific automatically breaks down qualitative answers from distractors, passives, and promoters, and summarizes followup input for each group separately. This lets you compare who’s happy and who needs support.
You can replicate these breakdowns in ChatGPT, but you'll need to segment data and paste responses group-by-group on your own—which is more time consuming and error-prone.
Dealing with AI context limits: keeping your analysis focused
If your survey has lots of responses, you might run into context size limits with AI tools like ChatGPT. This means not all answers can fit into the chat at once—which can block meaningful analysis of the full data set. It’s a common challenge.
There are two proven ways to manage this (both are built into Specific):
Filtering: Only include conversations where users replied to selected questions or chose specific answers. Filtered analysis lets you focus the AI on the most relevant subsets, like just those who reported technical issues, or students who gave neutral NPS scores.
Cropping: Limit analysis to a specific question or set of questions. Instead of sending all data, send only what relates to your area of interest—this means more targeted insights and avoids the context-size wall.
Learn more about these workflows and why they make sense for education survey research in this in-depth article on AI survey response analysis.
Collaborative features for analyzing online course student survey responses
Collaborative analysis is a real struggle when you’re running technical support surveys with a team—lots of data, busy schedules, and everyone wants clear, actionable results.
Analyze together by chatting with AI: In Specific, survey results become instantly collaborative. You just launch a chat with the AI and dive into results together—no need for endless Excel chains.
Multiple chats, each with filters and ownership: You can set up several chats, each focused on a different part of your technical support survey (like NPS detractors, responses about live chat support, or just students from a particular course). Each chat shows who created it, making teamwork clear and organized—ideal for product managers working with course instructors or tech support leads.
Visible collaboration: Specific displays who said what, with each message marked by the sender’s avatar in AI Chat. This transparency makes feedback loops and decision making easier, especially when your survey analysis is cross-functional—CX, instructors, and IT can all drill into the same data and annotate findings.
Actionable insights, not data dumps: This setup turns messy survey analysis into focused teamwork—so you address top challenges quickly, like the fact that 56% of online learners say instructor responsiveness is a key satisfaction driver, or the 55% who say poor support leads to course dropout. [1]
Create your online course student survey about technical support now
Get richer, more actionable insights from your online course students—ask deeper questions, collect smarter feedback, and let AI do the heavy lifting on analysis. It’s faster, easier, and you can instantly collaborate with your team.