Transparent AI Use Strengthens Qualitative Research (Full Transcript)

[00:00:00] Speaker 1: Welcome everybody to this webinar. As you can see, the topic is about AI-assisted qualitative research. I am very passionate about this topic. Why? Because AI is here to stay, and we as qualitative researchers, we have to find the best way of using it, and not only using it, systematically using it, and being able to communicate how you use it, so that everybody will know you are not hiding. Because one of the quality assurance of qualitative study is about dependability. So dependability is about being able to document any actions and decisions that you take, and being able to communicate what exactly you did. When you communicate what happened, people believe what you found, because qualitative study is more about subjective, you and your participants, you and your data. If you don't show your work, people are not going to believe what you found. So when using AI as a tool to help you to do your research, why are we hiding? We have to let it be clear to people how you use AI, so that people will trust what you found, and people will learn from your strategies, how you use AI to make sense of your data, how do you use AI to get literature for you to do your research. So that is all about this presentation, and it's going to be a little bit practical, because I want you to learn something. I'm going to focus on three main tasks. So it's about mechanical, analytical, and interpretive tasks. And at the end of the day, you'll be able to classify your research tasks, you'll be able to determine the roles that AI will play in your study, and also be able to know what exactly you have to do if AI does something for you, what are you going to do about that? So we're going to have a discussion concerning that. And lastly, there's a lot of AI tools, right? What are you going to do to choose the one that is appropriate for your activity, for what you want to do? So that's what I'm going to focus on today. And if you have any question, you can put in the chat, I'll be happy to address them if I have time. Yes, I will try. And also try to participate because what I've learned is that just listening to what I'm saying, you may not learn a lot, but if you're trying to be part of the process, I think you'll be able to learn much. So we're going to start with a little activity here. So think about what you are doing now in terms of a project. Are you doing any projects currently? Or do you plan to do any projects in your future? It doesn't have to be a research project, any project that involves maybe collecting information, involve gathering information, involve analyzing, involve documenting the process, any kind of project that will be helpful. Think about any project that you are doing, three to six activities. It doesn't have to be long, just a phrase, right? So I've given you an example here, transcribe 12 interviews, hold the focus group data, something simple, just list them now. We're going to work on that. We're going to determine which one is a mechanica, which one is analytical, which one is interpretive. So if you can list them now, that would be great. You can type it, you can write it down. I think that would be great. So I'm going to explain all three kinds of tasks so that we all will be on the same page. So the first one is mechanica. So think about mechanica is any kind of task that is related to preparing or arranging information. So think about your research. After collecting data, right, you have your audio. What did you do? You transcribed, maybe you didn't do it yourself, you used AI to help you. That is mechanica. When you are looking for literature, what did you do? You went, maybe use AI to help you to select relevant literature for your study. It's mechanica. Or you want to compile all the, maybe interview transcript. Maybe you want to transfer all of them into Excel spreadsheet. You can use AI to do that. It's mechanica. Any activity that is more about organizing, retrieving and formatting information is more about mechanica. And another thing is that mechanica, most of the time, you don't need any critical thinking. You don't have to analyze things before you complete the task. If you are manually transcribing it, you don't have to think through. You just listen and then write what the participant is saying. So it's more about preparing and arranging information. What if you give this task to AI? What are you going to do? You are going to verify. After AI has transcribed your audio for you, you have to listen to the audio because AI can make mistakes, right? Listen to audio and make the necessary correction. It's called verification. Verifying and making sure that everything is correct before you use. That is your role. Normally, most of the time, we give mechanica activities or tasks to AI to do because that doesn't involve a lot of thinking. So think about what you are doing, right? Is this mechanica? Can it give it to AI and you feel comfortable concerning that? What are you going to do? You have to verify because every output is your responsibility to verify, making sure that everything is right. So think about your activity, which of them is mechanica. Just note that, we'll come back. Just note, if you have any activity that you listed and it's mechanica, just note that activity. So let's go to the next one. So the next one is analytical. So think about this one. I think this one will be very easy for you to think through. When I think about analytical, I think about data analysis, right? What happened during data analysis? You have your transcript, you have your research question, participant has given you information. You have to extract information that are significant to help you to address your research question, right? In analyzing something, you have to break the information down. Break it down. It's just like you are investigator. You are looking for evidence, right? You pick this one, you pick that one, you pick that one. Like analyzing your data, you pick all the information that what is said that can help you to address your research question. You are analyzing. You pick what is relevant. You bring them together. You group them. You compare, you identify patterns. That is all about analysis. Think about this situation. In terms of analysis, will you give AI the chance to analyze your data for you? Oh, AI can do that. AI can analyze your transcript. But think about it as suggestions. AI is suggesting things to you. They are suggesting course to you. They are suggesting path. And they are suggesting relationship between things. And you have to evaluate what AI has given you. You have to examine and making sure that everything is right. Is this code directly come from the data or the system made it up? It is theme comes from the data. Can we trace the theme back to the data? That is your work. If that's your work, then you have to do something. You have to know the skill in analyzing data. Because if you don't know the skill in analyzing data, you know what is gonna happen? You accept what AI gives you. I've uploaded all my transcripts and I've gotten themes addressing my research question. I present that information. I didn't check. Why? Because maybe you are not knowledgeable about data analysis. You don't know anything about thematic analysis. So it's very important as an evaluator, you have to develop a skill in analyzing data before you give your transcript to AI to analyze. If you don't do that, you know what will happen? AI can make mistakes. You reported mistakes. And you are not doing justice to your participants. Participants give you rich information. So before you become evaluator, you have to be skilled in the subject matter that you are evaluating. If you don't do that, you're going to accept everything. Then you will not be needed anymore. AI can do everything for you. Then you have no control. You are losing your agency. And this is what is happening. If you don't take care and be a critical evaluator of AI output, you will not be needed anymore. And AI will take over the analysis. And that's why you always have to think about developing the skill, although AI can do this, so that you can even ask right question. For AI giving you themes, you can ask AI, where did you get this theme from? What are the evidence from the data to support this theme? AI have seen this path. Where is the evidence in data? Can you show me? You ask critical questions because you are knowledgeable about a subject matter. If you are not knowledgeable about a subject matter, AI will take over your job. And that's what I always preach. So if you want to know about data analysis, my book would be helpful for you. Let me quickly put the presentation in a chart if you haven't gotten it, and then you can download. If you go to the latter part, you'll see my book there, Step-by-Step Guide to Qualitative Coding, so that you'll be able to learn more about that. So let's move. So think about it. You have identified information that is significant from the data. AI has suggested to you two codes. One is after hours chatting based on this information. Another one is theme isolation based on this information. You have to evaluate. Is this one based on the data? Yes, but this one too. Maybe it's based on the data because it talks about isolation, but is this one addressing the research question? No, I have to refine that, change it a little bit to address the research question, representing information that has been extracted. Oh, there's another theme that AI didn't come up with. AI missed that. I brought that information. So you see how you are critically analyzing AI information because you know what a code stands for. You know how to evaluate a code, right? So the list that you provided or the list that you have, which one is analytical tasks indicates that. So let's go to interpretive, making sense, right? You're explaining what something means. This one, you see, I don't know whether you have seen this pattern. From Mechanica, AI can do a lot of Mechanica job. If Analytica, AI can do a lot, but you have to supervise. Interpretive, AI can do, but you have to be very careful because sometimes AI can make things up when it comes to interpretation. It will take into consideration some factors that may not be supported by the data. So interpretive, if you want AI to do that for you, you have to be very careful, critically analyze or review the interpretation. And if you want to review the interpretation, do you have the skill of knowing what is an interpretation? What is interpretation? It's a meaning, it's a conclusion, it's a claim, it's a speculation. All these things you have to think about it. What is a speculation? What is a claim? What is a conclusion? What is a critical review? So in case you want AI to make interpretation, then you have to review and decide whether you accept, reject or modify the interpretation. You can do it the other way around. You can interpret your findings and give it to AI to critically review or provide you feedback. You can do that too, right? The most important thing is that you have evaluated AI out to making sure that information is right. When it comes to interpretation, it's good to do it yourself. If you want to use AI, make sure that you have the skill in evaluating the output. AI is becoming very good. You know, at the beginning it started with Mechanica, maybe two years ago, and then we moved to Analytica. Mechanica aspect, when we were at that stage, AI was finding difficult to analyze data, but now it's very good. And now it's getting closer to interpretation, right? But we have to be very careful because it can misinterpret participant information. So you have to be skilled in analyzing that information. So at this moment, try and do it yourself and you can get ideas. You can see, you can view AI as like a critical aspect, having conversation, oh, I'm thinking about interpreting this way. What do you think about that, right? You can get ideas, but the final decision should be you. You have to decide. So in your activity, which one is an interpretive activity? So when you are interpreting, you have to think about evidence. So if based on the data or based on your findings, AI says that healthcare providers avoid work because they fear burnout, based on the findings. So what is the evidence? So if AI give you this information as for evidence, is there any evidence from the data to support that? In what context, in what situation, is there any alternative explanation related to participant avoiding work? Maybe they're avoiding work because they are tired. Maybe they're avoiding work because maybe there's other things other than burnout. You have to explore alternative explanation. That is your work as a researcher. So now, what do you think? So your projects, have you categorized them into Mechanica, Analytica, and Interpretive? You can put in the comment area there. Have you done that? Have you labeled them? Some of the activities, you may not be sure. It might be in between Mechanica and Analytica. It can be in between Analytica and Interpretive. Just share, and then we can have a discussion. What do you think? Any comments, anything? Or if you can work on it, you have about maybe a few four minutes to work on this one. Try to categorize your activity into these three. Okay, so Mohammed said, I had experience of Mechanica using AI for transcription. Yes. So using AI for transcription is not always 100%, but it can do the job. And then your role is to listen to the audio and make the necessary correction. Thank you. Okay, so I have a question. I'm trying to use AI to help create my interview protocol. Is that Analytica? Yes, it's Analytica because you have given the system the purpose of your study and your research question, and then you want it to create interview protocol for you based on your research question. So it's like it's using the input that you are giving and then analyzing it and coming up with potential questions. So it's more of Analytica. So this means that you have to know how to create interview questions so that you'll be able to assess whether this one is good or not. So interview question, make sure that they are in line with the purpose or the research question that the system provides. Also provide the system a lot of information. One thing is that if you're going to do semi-structured interview, you have to tell the system. You have to tell the system the purpose of your study and who you are going to interview. That will give the system a lot of information to give you rich information. Oh, what else? My study is to design a mixed method interview design categorizing this Mechanica. So think about it. Mechanica, you don't need a lot of thinking, reasoning. So it's more about Analytica because you are giving the system some information about your study and the system think through and provide you an output. So I will consider that as Analytica. Mechanica is all about repetitive work that doesn't need a lot of thinking, right? Sorting, transcribing, retrieving that information. So Grace, I use AI for transcription as well, okay? So if you use AI for transcription, it's more about Mechanica because AI is listening to the words and then providing corresponding texts. So it doesn't involve a lot of thinking. So I will consider that as Mechanica. The reason why you have to know all this thing is that imagine you finish doing it and then you are telling people, sharing your information and sharing that, how do you use AI? I use AI mostly for Mechanica, right? To transcribe my audios. And then in terms of Analytica tasks, I didn't use AI. So it's like, you know the kind of tasks and also what the role AI played. So communicating it becomes very easy for people to understand. And also people learn from you too. We are all learning from each other because AI is new. I want to know the skill that you are using or the strategy so that I can also do the same. So I use it for interpretation to see if it match my thought. That is perfect. So you have, yes, it's similar to like, you already have your own interpretation about something and then you give AI the chance to also interpret and compare. And maybe it also brings something up that you have not thought about. So the transcription, yes. Transcribing is Mechanica. Creating code is Analytica. Yes, creating code is Analytica because it's more about analyzing, right? Creating code book, right? Because it has to look at the evidence from the data and then develop codes. Yeah, grouping themes is Analytica. And you also have some interpretative area that it has a little bit of interpretive. Yes, you are right. So it's more Analytica when it talks about themes. Think about Analytica as like you are a reporter, right? You go and collect information, make sense of it and present that information to your audience. Interpretative is like, okay, now I have this kind of finding. What does it mean? What conclusions can I draw? Can I make a claim and then use the findings or participant information to support that? I can make a claim right now that my presentation is engaging. It's a claim. Now, what is the evidence? Oh, people are making comments. That's the evidence, right? I can see a lot of comments here. So that's what I think, right? But it's also different between assumptions and interpretation. So I think this is another thing that you may have to explore. One thing I use AI for is like, use it as like a learning device. Like sometimes I ask myself, oh, so what is the difference between assumptions and interpretation? What criteria can I use to determine whether this one is an assumption or this one is an interpretation? You know, you see how I'm using AI to learn. So use it to build your skills. When you are doing a systematic review of a qualitative paper, is it Mechanica or Analytica? It depends on what you're doing within systematic review. If you are retrieving information, you ask AI to retrieve relevant sources. It's Mechanica. If you want the AI to synthesize the sources, grouping them for you is more about Analytica, right? So it depends on what you do within systematic review. So what you're saying that you did a qualitative analysis and then you did it manually and then you use AI to do it and then you do the comparison. So yes, that is a problem that we've been having about AI cannot give you all the background information, what happened in the background, right? People who even develop AI still have a difficulty telling us what really happened in the background. They always say that it just predicts the next word, but it's more than that. Maybe in the near future we will know, but we don't want to wait until we know before we use it. That would be the perfect one, know before you use. But what we can do as qualitative researchers is to question the system. So if the system gives you code, you can say that, how did you come up with the code? Do you have any evidence in supporting this code? Can you show me in the data? So questioning, being a critical reviewer of AI information will be helpful for you. If the system gives you interpretation, you can say that, what are your basis? How did you come to this conclusion? Try to question the system, you will get some information that will be helpful. So yes, AI will not be able to give us everything that happened behind the scene, but we can use it as a tool to help us to reach our destination. If we have to wait for us to get a complete understanding of what happened behind the scene, then it's going to be a long time we will be left behind, right? And things are going fast. So I'm in a level of like balancing act, right? We cannot know everything, but based on what we know, we can critically use it in a way that will help us to reach our destination. So that's what I see the process. It's not perfect, but it's here for us to use. We just have to be transparent and learn from each other. How can I use AI as a personal assistant in my research? That's a good question. How can I use AI as a personal assistant? It depends on what exactly you want to do. So first, if you can list all the activities that you want to do within the research, then you can decide that, okay, this is the role that you want AI to play. This is my role and I'm documenting the process, right? And you have to be systematic because if you don't do that, people will not believe what you found. If somebody asks you, so how did you use AI to do your research? And then you will not be able to use, right? Yes, you can use this as your personal assistant, but as a personal assistant, what kind of activities is AI doing in your study and what are you doing about it? Let's look at it in a normal situation. You have a personal assistant at a workplace. Yes, the person is your personal assistant, but the person has a specific work that he or she has to do, right? So your role is to provide the person the work and then you supervise, making sure that person does their work in the correct manner. So AI can do that, but it depends on the job that you want it to complete. Okay, because I'm so passionate about this topic, I came up with a simple table that will help you to categorize or determine or learn more about AI tasks. So if you have your phone, you can take a picture here. If not, you can also click on the PowerPoint that I sent. I'm sending it again, if you just came. I'm putting the chat. So go there and click on it and let's click on it. Let me show you something quickly. So when you click on it, I developed this one. First, I developed this one in Cloud AI, and then I came to Chachibit to develop it more. And then I was able to create a link so that you can easily click on it and it can get there. So these are some of the tasks that you can do in a research. Okay, let me explain this one before I talk about other things. So you can just click on it, right? You see the task, Literature Retriever. It gives you the scenario. It gives you AI role, it gives you a role, and then what may go wrong, right? You can click on next and go to another one. This is for learning purpose. You can use it for your teaching and you can create this one yourself, right? You can use mine, what I have here, but if you want to go further and you have access to Cloud AI, head version or Chachibit, I can help you to create. So put in the chat whether you want me to give you information about creating your own customized one. I can help you now to create. It takes few minutes to help. So if you want help, you can type help in the chat. Okay, I see help. Okay, it looks like people want me to show, right? So those who are not technologically savvy, you can use this one. You just click on a link and then play with it, see the tasks and then review. Some of the tasks that you have may not be here, but just click on it and then learn from it. Those who want to create their own, you have to have access to Chachibit, paid version or Cloud. So I'm going to quickly put a code here. It's called scale. So let me share with you. So you see this one, it's a AI task periodic table scale. I'm going to put it in the chat. So you have to download it, right? And save it on your computer. And then you go to Chachibit or Cloud. What you can do is that you have two options. You can say that, can you use this scale to create periodic table for me? And it will create the same thing that I have here, right? So you attach it to Chachibit and say, can you use this scale? You can attach it to Cloud and the system will create something similar like this. If you want to go further, right? If you want to go further, you want to create your own customized table. You can say that I am maybe program evaluator. I work on this kind of project. Can you create a customized periodic table AI task for me? And a system will use this one as a base and then create your own customized one to fit what you want. Or I'm going to teach a class about qualitative research. Can you create a customized periodic table that will help a student to learn about a concept related to qualitative research? The system can create something similar to this for you. So I'll put the text in the chat that you are going to attach to Cloud or Chachibit and it will create something for you. You can do it now and then you can wait and maybe you can share at the end of the day when it's ready. It will take about five minutes or 10 minutes to create it for you. You can try it. And I'm very curious to see what you have done at those people who want to do it. But if you don't want to do that, you just click on this link and you get something similar like what I have here. So let's move on to the next one. So the last area that I really want to talk about is how do you determine tool that you want to use based on the task that you have? As you know, there are a lot of AI tools but I want to share my personal opinion to you. If you don't take a use with one project, you use about five tools. One for literature review, one for editing your manuscript, one for data analysis, one for, what again, presentation, one for, but you know, you save a lot of money if you just use one tool that can do all of them for you. You just have to develop a skill, right? In doing that. It's very simple. You can just try. I'm doing this presentation. Can you create a PowerPoint for me? At first, you do not create a good PowerPoint because the system way of thinking is different from yours. The logical way of structuring information that you want to communicate is different from what AI gives you because it represents all the information that's got it. So you know what I do? I tell the system, I want to present about this topic, that topic. And I want to present in this sequence. Can you develop a PowerPoint that follow the sequence the way that I want? Then you have something like what I have here. So there are three things. First, think about the analysis that you want to do or the tasks that you want to do. And then you look around and see that the software, that AI software that can help you to do that. Then you can try it out and see whether it's really suitable based on the output that you're going to get. But you also have to think about its risk and the cost. You are using all these AI tools. How are they going to use your data? Have you checked it out? Yes, it's good. It does many things for you, but find out how they use your data. Participants are giving you information you want to protect that information. So don't just jump and use AI without checking. How much does this cost? Can you afford? So all these things have to be taken into consideration. I'm going to show you a case study. My example, I wrote an article and I'm going to show you. So if you click on the document that I sent you, this is an article that I'm talking about. It has been viewed 51 times. It has been downloaded 17 times. I was trying to share, I want to get the message out there quickly. I don't even want to pass through a long process of peer review. And sometimes you have an idea and you want to publish it quickly. There are some things that you want to pass with peer review. If you want to publish something quickly for people to know and this is an option for you. So I was able to write this within one or two days and then I was able to publish. You can also download if you want to. I talk about being transparent and under the publication, see what I wrote here. I disclose how I use AI. This is the disclosure. I'm not hiding anything. Yes, AI helped me to do something here from the beginning to the end. I provided that information. That is what I want people to learn. Don't be afraid to share so that people will learn from you. Now, because I provided information, people might learn how to write an article quickly, but also in an impactful way. Let me show you how I did it. So I had an idea. I've been researching a lot concerning disclosure, being able to show your work when you use AI. And I developed a framework called ROAD. ROAD stands for the need to show what kind of question you ask AI, what kind of output that you got, how do you assess AI output to make sure that it's correct and accurate, what kind of decision that you made. Did you accept, reject, or modify? That's my framework. And I want it to be out there for people to know and learn. So I wrote that article. So let me show you the process. And I'm giving you just a summary because of time. So the process, I have that idea. I've learned a lot about disclosure. And what I did is that I went to Microsoft Web. So if you have Microsoft, you can do the same thing that I did. I just click on Details and start having conversation, right? So I start talking, but you also have to make sure that you, let me pause here. You have to make sure you go here and check here so that the system automatically punctuates when you pause and also filter any sensitive phrases, right? So that's what I did. I just start talking about disclosure, my ideas about the need to disclose your information, the benefit of disclosing when you use AI so that we all learn. So I start having conversation. This is not perfect. After that, I went through making sure that, you know, everything has been captured in a correct manner, right? So I review, and then when I finished, I got about two pages. I sent it to ChachiPT, Cloud AI, and then Perplexity AI. So this is what I did. So I sent that information to them, and I asked them, can you critically review my thoughts here, providing critical analysis, and also providing resources that will help me to cite sources that reflect what I said. And a system review provided, critically provided me information, right? And then I was able to collect all the information from these three areas, right? So I collected from ChachiPT, Cloud, and Perplexity, and then reflect upon it, and based on that, I use it to improve the document. Then I went back to Cloud, but this time I click on Add In here. You see the Add In? When you go there, you can search for Cloud, and then you list, and the Cloud will be here, so that you can chat with the system, right? Your Cloud, if you have Cloud account, and a Cloud will be able to make adjustments here. So I can say that, well, I have three topics here. Can you categorize all my ideas into three topics? And a system does it for you as you interact, right? So, and also, can you look for, I have a list of references that I've gotten now. Can you connect the references to my ideas? And a system did that for me, right? So as you chat here, the system does things for you here, and you review, making sure that everything is right. And then at the end of the day, I was able to come up with this article, and I've uploaded into the system. It's free to upload in that place. So when you upload, you get your DOI number, and then everybody can read, you share it, and then people read and give you feedback, and then, so that's what I did, right? So as I said, this doesn't replace a peer review article, but if you want to get your information out there quickly, this is an option, and this is, you see how I use AI to help me. So let me give an example of how I use Cloud so that I can see what I mean by that. Let me go here and open one of my, I wrote this one a long time ago. So in case I want to use AI Cloud, so I click on Cloud here, I can just say that, well, can you add us an editor going through this manuscript and making the necessary correction for me in terms of the grammar and what I've written it in a scholarly manner. You have to also do a track changes so that I can approve what you do. So this is the information, right? Let me click here, and the system will just go through your transcript. So you see here, it will go through the transcript and then edit it for you and provide, and you have to approve the edits, right? So you see how there are some software that can do this one for you, but if you have Cloud, it can do that for the same thing for you. And if you are worried about the Cloud using your information to train your system, it can go to your account and indicate that you don't want it to use it to train your system. So you see how the Cloud is doing now for me, going through and then changing stuff, right? So I have to approve the same thing as you give it to editor and it does it for you. You can even upload your previous article so the system to learn about how you write so that as an editor, it will edit it in a way that you write, right? So you can do that. And it's not a shame to indicate that AI help you to edit. It's not a shame always so that people will learn from it. I indicated that here. I think I wrote somewhere, right? Help me with the editing, right? In terms of formatting, alignment, AI stuff, citation. You see, you have to be open, but also be careful here because if your organization or your institution say that you shouldn't do it, don't do it. But if your organization allow or you can go and read about your AI policy for your organization so that you know how to use it. But this has been helpful for me. Thing that I want us to take from this one is the research that Antropic did. So Antropic, they are developers of Cloud AI, right? They did a research, very interesting. When you go to the PowerPoint and click on this one, it will take you to the research that they did. And this is a real example of being transparent how they use AI. See what they did. They wanted to find out from 1,250 professionals about how they use AI in the future, their perception about the future of AI. And you know what they did? They use AI to interview them. The software that they use is called Antropic interviewer. So they use it to interview them and they provide all the steps, how they even set up this system in terms of planning, how they plan the study. They use the system to plan the study. So this is what they did. They use Antropic interviewer to plan the study. So this means that they gave the system information about what they want to do and ask Antropic to use that framework to come up with questions that they're going to ask participants. After a system has been able to do that job, they interacted with the system, making sure that the questions are appropriate. Then they implemented it by sending the link to potential participants. Participants click on the link and have a conversation. The participants are informed that you are going to chat with AI, right? And the AI will ask you questions and you type the response. And then they ask participants, will you be okay for us to share your data for the world to know about how you use AI? Some of them said yes, some of them said no. Those who said yes, their data is available for you to use and run the analysis similar to what they did. So when you go up here, you go to this link, publicly releasing, you go to Hugging Face, you can download. So this is the data set. Some of them who approved that they should use their data to be available in public for the public to use. It's available now. You can download and then run the analysis and see what you're going to get. So you see how transparent they are. They told participants that AI is going to interview you. We want to share your information for the world to know. Will you allow it or not? Yes or no, you have that option. Yes, we will share. No, don't share, right? And then they also provided all how they analyze the data. You use AI to analyze the data. So the question to you right now is, after reading the article, no, you haven't read. So I'm assuming that you finished reading the article. Do you think that the value of what they found has reduced because they shared that they use AI? What are your thoughts? Do you consider this study as a scientific or proper study? What do you think about this? I just want to know your thoughts. So that's what I have for you today. If you can take a picture or you can look at it, click on a link and complete a survey. I'm planning to do this every two weeks or every three weeks to just help you to learn about using AI to do your research. So, you know, I just want to also learn from what you have learned today and that will help me to think about another topic. So that's what I have for you. My books are available. I don't want to go into detail. You just read and choose the one that you think will be helpful for you. And you can contact me. I provide one-on-one consultation and also provide training for organization about AI doing research. If your organization wants somebody to come and talk to your colleagues, I'll be there to help and provide that support. These are my contact information also. You can follow me or you can subscribe my YouTube channel. I provide a lot of information. So thank you so much for your time. Yeah, hopefully next, also follow me on LinkedIn because next webinar, I'll post it there so that if you are interested, you can book and then we have a discussion. Thank you all for coming. I appreciate your time. Thank you.