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How to use AI to analyze responses from ama attendee survey about discussion topics

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Adam Sabla

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Aug 21, 2025

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This article will give you tips on how to analyze responses from an AMA Attendee survey about Discussion Topics using AI-powered survey analysis so you can actually make sense of your data.

Choosing the right tools for analyzing AMA Attendee survey data

The approach and tools you use will depend on the structure of your data—what your survey responses actually look like.

  • Quantitative data: For questions like "Which discussion topic are you most interested in?" where answers are single- or multi-select, you can quickly tally up totals in Excel or Google Sheets. Simple, straightforward, and perfect for structured outputs.

  • Qualitative data: The real challenge comes with open-ended responses, follow-up answers, or comments. Manually reading hundreds of long responses is nearly impossible (if you value your free time). This is where AI survey tools shine—they turn raw text into usable insights by summarizing, categorizing, and capturing the nuance and frequency that human eyes might miss.

For qualitative responses, your tooling comes down to two main approaches:

ChatGPT or similar GPT tool for AI analysis

Copy & paste + chat—You can export your AMA Attendee survey data and paste it straight into ChatGPT or another large language model. Then you prompt the AI to analyze, summarize, or extract themes.

Limitations and convenience—Handling your data this way feels a bit primitive. You have to manage data formatting, context size, and prompt engineering yourself. Long responses may not fit within input limits, and iterating on questions is more manual. Still, it’s a low-cost and flexible way to get started if you’re tech-savvy.

All-in-one tool like Specific

Built for survey flow and instant analysis—With a platform like Specific, you both collect and analyze AMA Attendee feedback with a single tool. The conversational survey format automatically asks intelligent follow-up questions, so your data is higher quality from the start (read how follow-ups work).

Instant AI analysis—When results come in, Specific’s AI instantly summarizes responses and surfaces discussion themes—no need for spreadsheets or manual data wrangling. You can chat directly with the survey results, tweak filtering, and jump deeper into topics (just like in ChatGPT, but with more context control). The result? Rapid, actionable insights—teams using AI-powered survey analysis have seen up to a 90% reduction in analysis time, and a 25-30% improvement in data quality. [1]

Additional workflow power—You can easily manage which data is included in the analysis, filter by question, and drill down into response details, all in one place. For a hands-on overview, explore the AI survey response analysis page or try a ready-made AMA Attendee survey generator.

Useful prompts that you can use for AMA Attendee Discussion Topics survey analysis

Prompts are key when you're chatting with AI about responses—whether you're using ChatGPT or a survey-specific tool. Here are some trusted prompts for qualitative data from Discussion Topics surveys:

Core ideas extraction: Use this classic prompt to reveal the key themes in a block of responses. Works in both Specific and generic AI tools.

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

AI always works better with good context. Brief the AI on your survey’s audience, goals, and timing for richer analysis—here’s an example:

The survey was conducted with AMA attendees directly after a Q&A session. Our goal is to understand which discussion topics generated excitement, confusion, or follow-up requests, and segment responses by the attendee’s expertise level.

Ask for detail on a theme: Once you spot a topic (say, “AI ethics”), get the AI to dig deeper by prompting: “Tell me more about AI ethics responses.”

Validate specific topics: When you want to know whether anyone mentioned a certain subject:

Did anyone talk about breakout sessions? Include quotes.

Identify personas: Ask the AI to break out distinctive participant types:

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.

List pain points and challenges: Surface what attendees struggled with or found frustrating:

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.

Extract motivations and drivers: Discover what pushed attendees to participate or vote for certain topics:

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.

Run sentiment analysis: Quickly gauge the mood of responses:

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.

(AI-driven sentiment analysis, especially with NLP-powered tools, can reach accuracy rates between 80-95% depending on the tool—handy for seeing if your event landed well [2])

For more ideas on building better questions, check out best questions for AMA Attendee Discussion Topics surveys.

How Specific summarizes AMA Attendee qualitative feedback by question type

Specific’s handling of different question types is one of its core strengths. Here’s what happens when you analyze:

  • Open-ended questions (with or without follow-ups): Specific generates AI-powered summaries for all responses to each question, including follow-up answers. You get aggregate takeaways and granular details.

  • Choices with follow-ups: Each response choice gets its own summary, based on what attendees shared in follow-ups for that specific choice. This is great for understanding “why” someone made a selection.

  • NPS questions: For classic NPS structure, Specific splits comments by promoters, passives, and detractors—each segment gets its own summary, so you know what drove each score.

You could replicate this with ChatGPT, but it requires organizing your data by hand and managing separate prompts—more work, less insight per minute. For a deeper dive into this workflow, see how to easily create AMA Attendee Discussion Topics surveys or try creating a survey from scratch with the AI survey generator.

How to tackle challenges with AI context limits

AI context size is real— Large language models like GPT can only “see” so much data at once. When your AMA Attendee survey produces hundreds of long chats, you’ll hit a wall if you try to analyze everything in a single batch.

There are two practical fixes that Specific offers (but you can mimic them elsewhere with extra manual labor):

  • Filtering: You can filter conversations to include only those where users replied to a particular question or picked a specific answer. That way, you focus analysis on what matters and keep the data volume manageable.

  • Cropping: Only the questions you care about get sent to AI for analysis—which not only fits the context window but lets you see trends focused on a single topic or segment. This also increases the accuracy and relevance of AI-generated insights.

Industry research suggests that smart filtering and sampling, paired with AI, can cut survey analysis turnaround time by up to 70%—meaning you get actionable feedback while it’s still relevant. [2]

Want more background on this? See how the underlying survey editor works in Specific’s AI survey editor.

Collaborative features for analyzing AMA Attendee survey responses

Most teams struggle to actually collaborate on survey analysis—especially for in-depth conversation data from AMA Attendees who shared thoughts on diverse Discussion Topics.

Real-time chat with AI: With Specific, you analyze and iterate on findings in a familiar chat—instead of getting stuck with static, rigid dashboards. Results are fluid, and you can jump into side topics easily.

Multiple collaborative chats: Each team member can create their own analysis “room” with unique filters (focus on moderators’ comments, compare those new to the AMA, and so on). Every chat shows its creator, so you always know whose lens you’re viewing.

Transparency in conversation: While reviewing feedback or refining prompts, you see who asked which question—the sender’s avatar appears on every chat entry. That way your team shares hypotheses, tests new prompts, and builds a better synthesis, all in one shared view.

Frictionless to share: Sharing links or summaries from these chats is instant—just copy and share. For recurring events or continuous AMA improvement, you can compare results across months without duplicating work. (Want to see this in action? Check out the AI survey response analysis workflow demo.)

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Sources

  1. SuperAGI. Unlocking Actionable Insights: Top 10 AI Survey Tools for Data-Driven Decision Making in 2025

  2. Salesgroup.AI. AI Survey Tools: Features, Benefits, and How the Top Solutions Compare

  3. SuperAGI. AI-Powered Survey Analysis: A Head-to-Head Comparison of the Top Tools for Automated Insights and Recommendations

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.