Using AI in Phenomenological Research: Benefits and Limits (Full Transcript)

[00:00:06] Speaker 1: Hello, welcome everyone. So today, we're speaking of a very interesting topic. One that's even new to me. So, doctor, I do, I'm going to, I have some difficulties pronouncing this word. Phenomenal. What's the word doctor? I do.

[00:00:23] Speaker 2: Phenomenal approach. So, any kind of, you So phenomenological approach. You are not alone sometimes difficult to pronounce. Yeah.

[00:00:33] Speaker 1: Yes, yes. I can say phenomenal or so forth, but this one, right? So if you are also joining in and if this is your first time hearing about this approach, just let me know in the chats, if it's new to you or you've heard of it before, but it's still a foreign concept, put it in the chat so that we know today, right? So I think this is going to be very interesting for persons like me, Dr. Adu, what is this approach?

[00:01:06] Speaker 2: So phenomenological approach is all about identifying people who have experienced something. We call it a phenomenon, right? So they have experienced a phenomenon and then you want them to share their experience with you and then you analyze and address your research question that you have, right? So it's an approach that focus on analyzing participant experiences, right? It can be experience of drinking a coffee, right? You can experience of anxiety, experience of depression, experience of grieving, experience of, yeah, any kind of experience, right? And then what you are doing is that you give them participant a chance to share those experience with you so that you can analyze and address your research questions. So that's all about the phenomenological approach.

[00:01:56] Speaker 1: All right. So how is it different from other approaches when it comes to qualitative methods?

[00:02:02] Speaker 2: So when you look at other approaches like narrative approach, right? You are giving participants a chance to tell their stories, right? And stories can be the situation that they find themselves in. Story can be their reaction to a situation. But what is so unique about phenomenology is that you try as much as possible to give participants the chance to describe their experience, right? So as you are talking to, as we are having conversation, the information that I'm providing, you are experiencing the information. After that, somebody will ask you, what was your experience during the, you know, live event, right? And then you describe what you have experienced. So just, you know, it's more about, instead of focusing on a lot of things, areas related to a phenomenon, are focusing on only the experience of the participant for them to share that experience with you so that it can make sense and help you to address your question that you have.

[00:03:04] Speaker 1: All right. So based on what you have said, is it that certain topics are more suited for this approach?

[00:03:11] Speaker 2: Yes. So one example is that if let's say a person has mental health issues, right? and then they are trying to seek for support, right? You can do a study and find out what are the lived experience of people who have mental health conditions seeking for support, right? So whenever your focus is emphasizing on experience, then phenomenological approach will be the best for you. Right?

[00:03:44] Speaker 1: Okay, thank you. So if you're just joining us today, we're speaking about, what's the topic, Dr. Adu? We're speaking about?

[00:03:53] Speaker 2: Phenomenological approach, right? How to use AI to help you to conduct your study.

[00:04:03] Speaker 1: So remember, use the chat, just to say if this approach is new to you, if you have no of it before, and also where you're joining us today from, will be very curious to know. And if you're watching the replay where you're based and listening in from, right? So we have just gotten a definition of what approach is at least for me, it's very enlightening, Dr. Odu. So thank you. This is a new opportunity for me. And if I understood correctly, it's about, it's best suited for when you want to find out what's the lived experience of persons. So certain topics make it more adaptable than to be used, right? And in my area of work, when you're speaking about persons from marginalized community, I think this approach would be very good one to hear what the positive experiences are, right?

[00:05:00] Speaker 2: Yeah.

[00:05:00] Speaker 1: So you spoke about AI, right? And I don't see the connection immediately But how AI can help with the analysis or with this approach that you're talking about, person's subjective lived experiences. What does AI have to do with that?

[00:05:23] Speaker 2: Yeah, so think about AI as your research assistant, right? Anything that you have difficulty or you need a lot of time to do it, AI can help you to do that, right? So think about this one. Imagine that you are doing a research, right? And about experience of participants, and then you don't know the kind of the purpose, or you don't know the purpose of your study, or you are not, you are not really, you haven't constructed the purpose of the study well. You are not sure about what the purpose is, right? So you can tell AI, okay, I'm doing this research, I want to focus on, can you help me to construct a purpose of this day? And then the AI can help you, especially if you give a specific phenomenological approach that you want to use. Because there are two types of approaches, phenomenological approach. We have the descriptive phenomenological approach. We call it transcendental phenomenological approach. And also interpretative phenomenological approach, or interpretative phenomenological analysis. So imagine that you're doing a study. you have an idea, basic idea about what you want to focus on, but you don't know how to construct in such a way that is consistent with the specific phenomenological approach, you'd ask AI and AI will be able to suggest how to construct the purpose of your study. And also your research question, right? What question that you want to really address, right? And then you can say, okay, this is my purpose of, this is the purpose of my study. I'm using maybe descriptive phenomenology, can you help me to come up with research questions that are consistent with the purpose of the study?" And the system can provide you that information. So you see how it's the same thing as talking to an expert about your research, and the expert is giving you some information that will help you. You can ask the system the same question. The most important is to be clear about what you are focusing on, to be clear about the approach that you want to use and the system can suggest information that you can use and also review.

[00:07:41] Speaker 1: All right, thank you Dr. Aduf for giving us a little bit more detail. Now I'm looking in the chat and I'm happy to see there's someone from Mali, thank you for joining us. Clover, hi, thank you, it's nice to have you here. Yes, Gloria from Kenya, thanks and thanks for the nice words Gloria. So for everyone joining in, you will have someone from the UK. So thank you so much. And Michael, welcome. Put your questions, comments in the chat. And also there will be a QR code. If you want to join the discussion, you can come on stage and give your comments.

[00:08:19] Speaker 2: I think somebody is on stage. Let me try and see whether we can hear. Let's see. Oh, the person has left. Also, we can continue.

[00:08:38] Speaker 1: Okay. All right. So, as I say, feel free to put in the chat. We're speaking today about phenomenological approach.

[00:08:48] Speaker 2: Yes.

[00:08:50] Speaker 1: Thank you. I do. I stumble over that word, you know, I say, okay.

[00:08:59] Speaker 2: It's difficult to pronounce that. So, yeah.

[00:09:02] Speaker 1: All right. So you were speaking early on how AI can assist you with your research question and basically interview questions and so forth as well. What about like when it comes to the analysis of the data or even data collection, you know, is AI a good assistant for that purpose as well?

[00:09:24] Speaker 2: Yeah, you can use AI to help you to collect data. There is a software that, you know, can help you to, sometimes it depends on whether the participant will feel comfortable with the AI asking them questions, right? Imagine that you give the AI system the questions that you want it to ask participants, and then you send them the link to the interview information, and they click on the link, and then AI interview participants, right? It's possible to do that, but sometimes you have to also think about, will participants feel comfortable talking to AI being interviewed, right? Maybe as time goes on, people will feel comfortable having interaction, you know, their live experience, sharing with the AI, but I think what can be done is for now, what is very useful is for you to develop the interview questions using the AI tool and then meeting participants and then having a discussion with them and collecting information from them. I think that will be very useful. And after that, you can also use AI to help you to analyze your data.

[00:10:45] Speaker 1: Okay, all right. Thank you, JP Watkins. Thank you for tuning in. Thank you for joining us. So Dr. Odu, you were speaking about what I think is an AI agent. So this is AI asking questions and so forth, right? But based on how you have explained the approach to me, Is AI equipped to capture like the nuances of when interviewing someone, right? And the whole subjectivity of it. So is this really an approach that you can leave to AI to do the interviews?

[00:11:25] Speaker 2: Yes, so it's possible. Let me show you the website of the place that the tool that is being used right now. And I think that as time goes on, a lot of people will feel comfortable. So it's called AI Lies. It's difficult to pronounce that. So this is the software, right? So what happened is that, you know, when you can easily, when you go to try, right? And then you go to AI interviewer, this one, you could you know and if you know you allow the system to so you could you could try it out and see how it happens right so this means that you as a researcher you develop the questions you tell in a sister you're going to use semi structured interview questions right you give the system the questions and then assist system have interaction with participants. And then based on participant responses, you know, the system will provide further questions, right? It's similar to how human beings does it. So AI is trying to mimic the way we do research. So it's really possible for you to do that. And as I said, the only limitation is that, will people feel comfortable using, you know, having conversation with the system like this, right? And I think it would take some time for you to do that, for people to feel comfortable. But I think that for now, what could be done is to you doing the interview yourself with the help of AI developing the interview questions for you, and then having communication with the system. I think it's going to be the best way to do right now. So in terms of subjective and objectivity, we know that when it comes to sharing of experience, it's a subjective experience, right? But the thing is that AI is just helping you to make sense of the information. And if you provide AI information about the approach that you are using and how you want the system to analyze the data, it will do things that is similar to how we do the analysis. And you are there to also ask further questions and also ask the system to maybe review the findings and making sure that it's addressing the research questions. So it's really a possible way. It's possible for you to use AI to do the interview and also analyze your data.

[00:14:11] Speaker 1: Yeah. Thank you for sharing that website, right? So you can go and check it out. Maybe Dr. Adu, you can maybe type it in the chat. so persons can see which link to go to. Yes, so- You are just joining us. We are discussing how you can use AI to assist you in your research. And Dr. Adu just showed us where you can use AI to collect the data for you, to interview persons for you. So Dr. Adu, you were saying that maybe persons are uncomfortable right now speaking with AI, but who knows? able to be more comfortable because I would imagine maybe if it's a very sensitive area, a very sensitive topic, maybe they feel more comfortable speaking to AI than a human being that they think might judge them. Who knows, right?

[00:15:08] Speaker 2: So I'm just going to put it there. It's so true because I think that sometimes, as you said, experience can be sensitive, right? One example is domestic violence, right? it might be a sensitive topic to have a discussion with participants. Imagine that they have, there's a tool where you can have conversation with the system, right? I'm hoping that, you know, nobody is there and seeing your face and having a discussion, but the system is just, you know, gathering the verbal kind of communication you are having with the system, right? And I think that, you know, it's true, it will help people to open up and to share their sensitive information that you know meeting them one-on-one you might not be able to get it that information yes so it's so true it's possible so there's pros and cons yep using AI yes so oh I see there's someone on stage yes someone on stage let me see whether hi Mohammed can you hear us hello yes I can hear you Philip, can you hear me? Yes. Do you have any question for us? Thank you for joining us.

[00:16:24] Speaker 3: Yes, and thank you for this insightful session. I have been following you for a while on YouTube. Thank you for the insightful information that you provided us.

[00:16:33] Speaker 2: You're welcome. And thank you for following me on YouTube, too. So I really appreciate it.

[00:16:40] Speaker 3: It's clear that AI tools, such as the one that you have just showed us, have possibilities for enhancing data collection and analysis. But I'm struck by the importance of retaining the human element in this kind of research, especially the phenomenological inquiry. So my question is, how can we best balance the benefits of AI assistance, as you have called it, and with the crucial role of human interpretation and understanding in this kind of research?

[00:17:12] Speaker 2: Thank you for your question. You know, I think that the ideal way of doing research is to, you know, go and collect your own data, analyze and present your findings, right? So, but because sometimes, because of time and resources, right, limited time and resources, you ask yourself, what are the places where, you know, I can involve AI in the, you know, helping me to make sure that I can collect rich data from participants, right? So the human element will be where you have one-on-one interview with participants. But AI can also help you to get ideas about the kinds of questions that you're asking participants. Imagine that you are doing a study and then you want to use interpretative phenomenological approach. And you have the purpose of your study, you have your research question, you have questions that you're going to ask participants, but you are not sure whether they are consistent with the research question that you have. Can AI suggest some questions for you that you haven't thought about, right? So you are still a leader of doing the research, but the AI is supporting the process for you, things that you haven't thought about, right? And sometimes the question that you may try to to ask participants might be so complex that maybe they will have difficulty addressing or answering. Can AI suggest a simple way of you asking the same question? And AI can suggest that for you. So we see how when we are going through research, right, there are some difficulties that we may face that AI can help you to accelerate the process or help you to address that difficulty. right? And I think that it's not like AI leading the research. You are still leading the research, right? But there are some places that AI can make it better for you, right? So that's what I can see. So there's always a human element. You are not given all the work for AI to do. You ask yourself for the interview question that I want to ask, is that okay? Can AI can suggest something for you? So involving AI in doing some of the aspects for you even make your study richer and make the information that you want to gather from participant richer, right? Imagine that you have interview participant, you have a lot of data to go through. Sometimes it's difficult. You might miss some information from the data when you're developing themes. What about AI helping you to identify information that are significant that can address the research question? And you as a researcher review that information and making sure that they are addressing the research question, right? So you are now a reviewer and a verifier of the information a system provides you. And that's how, you know, or we can involve AI so that we can accelerate the knowledge that we are trying to get and develop and contribute.

[00:20:35] Speaker 1: Thank you, Dr. Adu. Yes, Mohamed, did that answer your question?

[00:20:41] Speaker 3: It does, yes. Thank you for such an insightful response. Yes. You're welcome. Yes.

[00:20:47] Speaker 1: I guess my takeaway, Dr. Adu, is at all times, you as the human is the lead. And AI should always be the assistant. With two humans as a lead researcher, you are in charge. So you have to validate, double-check everything that assistant gives. And the final responsibility is with you, the lead researcher, right? So I am seeing a few questions. in the chat, and one of them touches on ethics. So JP Watkins asks, how can you ensure that private and sensitive data is not sold or misused? On AI.

[00:21:30] Speaker 2: Oh, okay. Yeah, I think, you know, it's just, you know, I always say you have to act responsibly, right? Participant has spent time with you giving you rich information. So it's your responsibility to protect participant information. So what can you do to protect participant information? I think that the first thing that you have to think about is that can I take out all identifiable information that will expose who you talk to, right? Like their names and their location and other things that you think that when people get access to the information, right? It's called adversely affect or the credibility, not credibility per se, but privacy of participants, right? So after taking all the identifiable information, you have to look for a very responsible software, right? So software, so you always have to use the ones that a lot of people are using, right? The ones that are not, like the one that has been have some kind of credibility like AI, like the chat GPT, you can also go to the privacy session and then the terms of use and everything and see whether your data will be used to train the system, right? And for chat GPT, you can tell the system or indicate in the setting that you don't want your data to be used by the system. So you take all these actions so that you protect participant. So you have to know whether your data will be used and then you can decide not to allow the system to use your data. Another one is that when you go to the privacy setting, you can even copy all the information and put it in maybe AI for AI to summarize the main points for you so that you don't have to read everything. So you know that, okay, what is this system going to use my data, right? And then based on that information, you can decide whether you want to use a software or not. So you also have to check with your organization or your institution. Find out what's their perception of the policies concerning AI use. If they are telling you not to use AI, you shouldn't use that, right? But if they tell you that you should use, you have to find out how you're going to use it. Can you use it as a data analysis tool? Can you use it to help you to maybe generate research questions or interviews? You have to think about all these things before you use the AI tool. It's new, things are changing, but as time goes on, we all agree on the best way or the best practices in terms of utilization. But this information will be helpful so that you use it in a way that is responsible.

[00:24:25] Speaker 1: Yes, so take away, check the policy of your research institution or your organization and be guided by that. And if they allow you to anonymize the data, protect person's information before you upload and their settings, you said, Dr. Adu, on some of the software where you can change the settings so that they don't use the data to train, right? So I see other questions coming in from Dr. Robinson. Can this software, I'm not sure which software, synthesized, completed, phenomenological research into our publisher-ready article?

[00:25:11] Speaker 2: It can, okay, so, you know, the software, I think you are talking about IELISE, I find difficult to pronounce this, it's A-I-L-Y-Z-E, right? A, E, lies or something like that, A, lies. So this software, you can use it to collect your data, right? You can use it to analyze your data, develop themes, right? And to help you to address your research question. In terms of making it a publishable article, right? You know, I am a person who always advised that don't use AI to write your documents for you to publish. is so obvious that, you know, because when you are publishing, you are showing that it's from you, you collected your data, you analyze, you wrote the information, but you don't want to deceive people to seem that you did it, but AI did it for you. What you can use AI for is to help you to, you know, analyze your data and maybe help you to think about the kinds of information information that you have to put in the article, but not write the article for you. So in terms of data analysis, you can say that, you know, you just have to be transparent by saying that you use maybe chat GPT to help you to make sense of your data. And these are the problems that I use to access system. And then when I got the information, I reviewed to make sure that they are addressing the research question, right? And these are the thing that I came up with, right? So although the AI developed a themes for you, you are being transparent and telling people that this is what AI did. In terms of your writing, in terms of explaining what a team represent and the features of the team, you have to write it. Yes, you can use AI to help you to edit, right? Your document, but not write it, the article for you, right? So I think that the role that try to make it this way that AI is just your assistant helping you to accomplish a research goal, right? Doing specific aspects and where you can be, if people ask you what role AI took, you'll be able to be transparent and share with people. I think thinking about the way it will be very helpful.

[00:27:41] Speaker 1: All right, great. So I hope that answers your question. And since we're on data analysis, In your view, Dr. Odu, is AI better at extrapolating, maybe, identifying themes and patterns, especially when you're talking about lived experiences? Can AI do that more efficiently than traditional methods of data analysis?

[00:28:11] Speaker 2: Yeah, the traditional method will be the best, especially if you don't have a lot of data. is sometimes if you want to use AI, it depends on the model that you are using. If you are using old models, then you're not getting a lot of information from it. If your prompts are not good, then you're not getting a lot of information. Because it's all about a conversation that you have with the system, right? Imagine that you just put the data there and say that, okay, I'm doing a phonological approach, develop things for me. You'll get general information. But what about giving the system some background information? That, OK, I'm doing a phenomenological approach. I collected data from these participants. And this is maybe the demographics for you to get brief information about a study. Can you go through the data first, identify information that are significant, and extract them for me? After the system has extracted, you can say, can you review and make sure that everything is from the data? So you see how continuously asking the system specific question and also questioning the output is the best way of using AI. Not like, okay, I have my data, can you develop things for me? You get general information, right? So you have to play an active role and you have to have the skill in asking the system the right questions and so that you'll be able to get information from it, from the system, right? So it's all about having good communication with the system and also check whether it is one of the new models, right? And when I go to my YouTube channel, I talk about how to use the AI tool to analyze your data in an ethical way. So when you search my name, Philip Edu, you can go to my YouTube and get all the information that you need about analyzing your data.

[00:30:06] Speaker 1: All right. So definitely check out Dr. Adu's YouTube channel. And earlier when you were speaking about knowing the right questions and prompts, you know, we do have a workshop on that and there's a QR code on how you can join that workshop where we go in depth in how to write proper prompts because a lot of people are using AI nowadays but they're not using AI effectively. And to get the best out of AI, you have to get better at your prompts. And prompts is just queries that you ask AI, but instead of saying a question or a query, we say a prompt. So that's not the best practice. The better your prompt, the better your output. Now, Dr. Adu, do you have any, earlier you were speaking about clarity, that when you're interacting with AI, the clearer you are, the better, and your prompts. Are there any other best practice when someone is looking for a research.

[00:31:08] Speaker 2: I'll be happy to share my screen so that I can show you, I'm going to use ChatGPT. And I think that will help you to get an idea about, imagine you are doing a study. Let me go to ChatGPT. I think you can see my screen, right?

[00:31:27] Speaker 1: Yes.

[00:31:28] Speaker 2: Okay. So you can use the normal GPT-4 if you have a paid version, right? GPT 4.0 to answer these questions, right? But if you want your work to be easy, I have a software that I created under charge GPT called Qualitative, let me open it for you, Qualitative Inquiry Guide, right? So this one will help you to develop interview questions for maybe you are doing a focus group or interview, you can use this software and that chat GPT for you to use, develop questions for you to use to collect your data, right? So let's do one example and see how you can go about it. Imagine that, let me pull this one up quickly so that you see what I'm talking about. So let me see, imagine that you are doing a study, right? And let me make it a little bit bigger so that you see. Can everybody see my Word document? Can you see? Okay, so you are doing a study, transcendental phenomenological study, right? And imagine that the purpose of this study is to explore the lived experience of young adults with mental health condition regarding their health-seeking behaviors in a community with high level of mental health stigma, right? So you see that, imagine that you have this purpose, right? And then you have this research question that is consistent with the purpose of the study. Can you tell the system to give you some of the questions that you have to ask participants? What you can do is that you can copy the research question and the purpose of the study, and go to chat GPT and say that, can you develop Questions, I should ask participants for my transcendental, it's also called descriptive phenomenological approach, right? So for my transcendental, Transcendental Phenomenological Study. And then you can see here. research my purpose of the study and research questions and then you put a purpose of the study in a research question there and then a system based on this a system can generate interview questions for you so you see how the system is generating an interview question question that you have to ask participant. The main question and also the follow-up. Not the follow-up. I think this one is giving you why you should ask these questions, right? Because you indicated that the approach that you are using a transcendental, the system is going to ask participant a question about specific experience, right? And, you know, So imagine that you are not using AI. You may come up with questions that maybe some of them might not include the things that AI has suggested to you. Sometimes what you can do is that if you already have your question, you can ask the system, OK, these are my interview questions. Can you review and make sure that they are consistent with the research question? The system can review and give you some feedback. So you see how the process will be easy if you, you know, provide a system, the pair posts, the research question, and the system will be able to provide you this information. So if you want to get access, after our conversation, I'll put the link of this information in the comment section. You can get access. Let me try right now, whether you'll be able to get access for YouTube. I can keep the link there for Facebook. Facebook, I can provide a link for linking. I have to finish, when I finish, I put a link in LinkedIn for you. So that's how you'll be able to use this software. And I have another one called, let me open that up. This one will help you to analyze your data. So this one is Phenomenological Data Analyzer. So what happened is that you give the system background of your study and then attach your interview questions and or interview transcript and then you start you know asking the system can you look to and extract information that is addressing this research question for me and the system will be able to provide you all this information. So I'll put this link there you can try it out and also provide me some feedback but I think that the lesson here is that without providing the system a lot of background information about your study and being specific about what a system to provide to you, you will not be able to get rich information from it, right? So it's very important for you to be systematic. We call it, you know, last time I talked about chaining prompting, right? So the chaining prompting means that you ask one question at a time and ask the system, when the system responds to your answer or respond to your question, then based on the result, it asks further questions. So one example is that maybe you can tell the system for this one, you can tell the system, Can you review the interview questions to make sure they are consistent with the research approach? and questions and my research question. So you see, the system have provided you an output. You want the system to do a little bit of reflection, right? And then it will go through and review and then give you some feedback, right? So you see how you can easily refine your interview questions based on Accident System to do some kind of self-reflections and provide you some information, right? All right, thank you for the demonstration.

[00:38:55] Speaker 1: We are right upon time. So in summary, give context, do chain prompting. What is chain prompting? We'll tell you more about that at the workshop. You'll get more practice. you will get datasets to improve your interactions with AI. If you are using AI already, are you using AI effectively? Come out to the workshop. We are closing registration soon and you can get more details. If there are any final questions, now is the time to place them in the chat because we're wrapping up. We have two more minutes before we go. Dr. Odu, is there a question you want to address? The last question?

[00:39:42] Speaker 2: Yeah, there's a question here. I listened to your discussion, but I don't see any phenomenological aspect. AI and phenomenology contradict. Can you please respond to this? OK. So phenomenology, as you all know, is about studying people's experience. They have past experience of anxiety and other things, and then you want them to share their experience with you. What does AI got to do with this one? AI can help you to come up with questions that you want to ask participants if you provide AI system about your specific approach that you are using and then the purpose of the study, right? So there's no contradiction because you are not telling AI to develop experiences for you. AI is just a tool to help you to come up with questions that you want to ask a participant. AI is just a tool to help you to make sense of your data, right? You are still driving the research process. You are still making sense of the information that AI is providing to you. AI is just playing a minor role in helping you to make sense of your data, right? And so that you'll be able to get rich information to address your research questions. So in terms of the role, each phenomenology is playing its role as capturing the experience of participants. AI is helping you to make sense of your data, or AI is helping you to come up with questions to help you to ask participants to get rich information from them?

[00:41:32] Speaker 1: Great question, Behem. I think that was a very good question, because in the introduction, you've said, but the approach is about lived experiences and so subjective. What does AI have to do with that? Can AI capture the nuance? But you explained it well, that yes, there's no contradiction, because you are still the human and the lead researcher in all of this. Now, we're right upon time. Dr. Odu, I'm going to give you the last word before we close.

[00:41:58] Speaker 2: Oh, I think the last word is we are having a workshop in October, and I'll be able to have one-on-one with you to address all your questions that you have. If you want to be knowledgeable about using the AI to do your research and evaluation, this one will be very helpful for you. The future about AI, I think it's all about AI agents. So just think about this one. It's my come to pass. Imagine you want to do a research. You have an AI agent. You tell the agent, can you do this research for me? Can you gather information, analyze, and present the findings for me? And you just wait for the system to provide you that information. AI will go look for participants, interview them, transcribe, analyze, present the reports to you. So I don't know whether, what role you're gonna play in this one. Will AI take over the research process? What are we going to do at that time? So these are the things that you have to think about. It's, there's a positive side, it's like you still gonna be in control because it's like asking somebody to do some job for you and then you have to evaluate. So your role can be an evaluator of the information that I have brought to you. You are in control. You are the head of the research. You receive the information and now ask more questions. That's what maybe the role that will be played in the future.

[00:43:31] Speaker 1: Well said. With that, thank you all for joining in and for your time and see you at the next live session.