How to Use MAXQDA AI Assist for Subcodes and AI Coding (Full Transcript)

Learn how to generate subcodes from quotations, refine code suggestions, and use AI coding to auto-extract and link relevant transcript segments in MAXQDA.
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[00:00:00] Speaker 1: Welcome to the next video on using AI assets in MaskEDA to help you to make sense of your data. So the next step is when you have your research question and you want to develop subcodes under the research question or you want to develop codes under the research question, this is the strategy that you have to use right. So in this situation you have already gone through the data, identified information that are significant and then you want to develop codes right. So let's go to MaskEDA. So as you can see I have my five participants here. You can see that for research question one I have 14 quotations that I have extracted from the data and then for the second research question I have 12. Imagine that you have already created containers for the research question and then gone through the data, extracted information that is significant, but you haven't developed any codes yet. What are you going to do? You can use AI assets to help you to develop codes under each of the research questions. So how do you do that? You can easily right click on the research question, but the step that you have to take before you do this one is to make sure that you have extracted all the significant information and connected to their respective research questions. As you can see here I have 14 significant information. I right click here and I go to AI assist and then I go to suggest subcodes. Click on that and then what the system is going to do is going to go to all the 14 significant information and then generate codes under representing the significant information. So you click on OK here and then within a few seconds you'll be able to get your results. So as you can see here the system has suggested a lot of codes for us. We have one, two, three, four, five and also other. And then each of them when you click on it, it also has the definition of the code and examples of the significant information that are really connected to the code. And then it also brought about gave information about delimitation. So as it has given you all the description about a code including delimitation. You see here giving you work-life imbalance, the definition of that and also examples and also delimitation. So if you are okay with them, you can click on all and then to move to their side. You can always delete what you don't want. Let's say you don't want other, you can click on it to delete it. If you don't want it, you just click on it to delete. So you have the chance to delete what you don't want. And when you are done, you click on create codes. And then when you do that, what will happen is that the information will come right under the research question here. Right. So I have all the codes here. There's a little limitation here that there's no information or significant information that has been dropped into all these codes, right? So we don't know out of the 14, which of the quotations are connected to each of the codes that we have here because the system wasn't able to do that. What you could do is that you can double click on the research question container and then go through them one by one and then drop it into their respective codes. Right. So you can see that long hours, you can select and drag and drop it into this code, which is called workload and time pressure. Right. You go to the next one. This one, numerous clinical and administrative tasks is related to administrative burning. So you see how you can easily go to and manually select information that are significant and drop it into their respective code. I like this process because it really forces you to be active in data analysis process. Right. The system has suggested codes for you, but you have to go through all the significant information and drag and drop them into their respective code. Right. So it helps you to play an active role in the data analysis process. That's what I like about it. But if you really want the system to go through the transcript and extract information and connect them to the code, you can also do it by you can select all the codes here. Right. And then let's say I want the system to go through all the transcript here and extract any significant information addressing the code workload and time pressure. Right. What are you going to do? You first select all the information that is significant, all the transcript data you want the system to go through. You right click on it. You click on AI assist. So you right click and go to AI assist and go to AI coding and here make sure that all the document of interest has been selected. You click on OK and then you drag and drop workload and time pressure here. You see here that the system has also brought information about what this code represented, examples of that and delimitation. This one will help the system to look through and extract information that is truly addressing or extract information that are truly linked to this code. So what will happen is the system will go through all the quotations, all the transcript and extract information that are significant and then connect it to their code workload and time pressure. So you can see that 10 significant information have been extracted from the five documents. You click on OK and then you can see here 10 comes here. You can double click on the review, double click on the code and review all the quotations and see whether everything is addressing every such question that you have. You want to learn more about MassQDA? This webinar, House on MassQDA Training is a two-day training from April 27 to April 28 from 10 a.m to 2 p.m and then if you want to be part, you can click on a link in my description and you'll be able to register and then I'll put a promo code there. You get 25% off if you use that promo code.

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Arow Summary
The speaker demonstrates how to use MAXQDA’s AI Assist features to develop codes/subcodes from previously extracted “significant information” (quotations) organized under research questions. After ensuring all relevant quotations are linked to the correct research-question containers, the user can right‑click a research question and use AI Assist → Suggest subcodes to generate candidate codes with definitions, examples, and delimitations, then select/delete unwanted suggestions and create the codes under the research question. Because suggested codes are not automatically populated with the quotations, the analyst should manually drag-and-drop each quotation into its appropriate code, staying actively engaged in analysis. Alternatively, to have the system search transcripts and attach relevant excerpts to a selected code, the user can select documents, open AI Assist → AI coding, and drag a code (e.g., “workload and time pressure”) into the AI coding panel so MAXQDA extracts and links matching segments for review. The video ends with a promotion for a two‑day MAXQDA training webinar with a registration link and discount code.
Arow Title
Using MAXQDA AI Assist to Suggest Subcodes and Auto-Code Data
Arow Keywords
MAXQDA Remove
AI Assist Remove
qualitative data analysis Remove
research questions Remove
coding Remove
subcodes Remove
quotations Remove
significant information Remove
suggest subcodes Remove
code definitions Remove
delimitation Remove
drag and drop Remove
AI coding Remove
auto-coding Remove
webinar training Remove
Arow Key Takeaways
  • Link all extracted significant quotations to the correct research-question containers before using AI features.
  • Use AI Assist → Suggest subcodes on a research question to generate candidate codes with definitions, examples, and delimitations.
  • Review AI-suggested codes, delete irrelevant ones, then create codes under the research question.
  • Manually drag-and-drop quotations into the appropriate codes to stay engaged and ensure analytical accuracy.
  • Use AI Assist → AI coding to have MAXQDA scan selected documents and attach relevant excerpts to a chosen code, then review the results.
  • Always validate AI-coded segments by opening the code and checking whether excerpts truly match the code scope.
Arow Sentiments
Positive: The tone is instructional and encouraging, emphasizing efficiency and the value of staying actively involved in analysis while using AI to speed up code development and excerpt retrieval.
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