Microsoft Learn Student Ambassadors Program Interview Series Episode 2

by Contributed | Jun 29, 2024 | Technology

This article is contributed. See the original author and article here.

Welcome to the Microsoft Learn Student Ambassadors Program Interview Series!

In this series, we will hear and learn from current and old Microsoft Learn Student Ambassadors about their personal experiences with the Microsoft Learn Student Ambassadors Program and why they think students should join and be a part of the program and community. 

Today, I will interview Deepthi Balasubramanian, a Gold Student Ambassador from India. Who is currently a final-year student pursuing a bachelor’s degree in Computer Science and Engineering at Vellore Institute of Technology, Chennai (India). His interests lie in AI, Cloud Computing, and Software Engineering.

How did you get to know about the Microsoft Learn Student Ambassadors program?

As a freshman at my university, I joined the Microsoft Innovations Club. A senior from the club introduced me to the Microsoft Learn Student Ambassadors program, which immediately caught my interest!

When did you join the Microsoft Learn Student Ambassadors program?

I became a Microsoft Learn Student Ambassador during my sophomore year, in October 2022.

What activities have you contributed to as a student ambassador in the Microsoft Learn Student Ambassador program?

This program’s goal of empowering students to ‘be a force for good’ aligns perfectly with my goal of making technology accessible to all. It has provided me with a comprehensive platform for skill-building, community-building, and networking, which is essential for my professional growth. And also the opportunity for me to collaborate with Microsoft Most Valuable Professionals, and fellow student ambassadors,

I have organized over 16 workshops and events on technologies such as Azure AI Studio, Copilot, Responsible AI, Power Platform, and GitHub, reaching nearly 1500 students and promoting inclusivity in tech.

I have actively led two teams in Ambassadors AI project cycles, winning one cycle and placing among the top 5 teams in another. During these cycles, I developed social impact projects and presented them during demo days. Recently, I served as a Gold Ambassador judge for the AI project cycle held between March and May 2024.

As a peer mentor, I’ve guided fellow ambassadors, enhancing their program experience. The recognition and appreciation for our efforts from the Microsoft Learn Student Program have helped foster a strong sense of inclusion and value within the community. Overall, being a Microsoft Student Ambassador has been incredibly fulfilling.

Being a Microsoft Learn Student Ambassador how are you able to manage your time as a student?

I’m always drawn to opportunities that enhance my skill set, like being a Microsoft Learn Student Ambassador. With effective scheduling and prioritization of my tasks, I can manage my academic workload and allocate sufficient time toward every task or responsibility.

The Microsoft Learn Student Ambassadors program supportive environment and flexible time frames makes it possible and easy for me to navigate between the program and my studies. 

What have you benefited from the Microsoft Learn Student Ambassadors program?

The Microsoft Learn Student Ambassadors program gave me an identity as a tech leader, provided me with the resources to learn and also skill learners on industry-relevant skills.

This community values every individual and offers extensive opportunities for personal development. Giving us direct mentorship from  MVPs, cloud advocates, and experienced FTEs  which has given me invaluable insights into the industry’s technical landscape.

Connecting with passionate students globally who share similar goals has been incredibly motivating, and the opportunity to collaborate with fellow Student Ambassadors.

Additionally, as an ambassador I enjoy access to a variety of perks, including digital and physical swag items.

What advice do you have for students and why do you think they should join the program?

The Microsoft Learn Student Ambassadors program offers extensive opportunities for learning and growth. I encourage students passionate about technology and eager to empower their peers through technology to join this vibrant community.

You can join the Microsoft Learn Student Ambassador Program by applying here 

Check out the previous series 

Microsoft Learn Student Ambassadors Program Interview Series Episode 1 – Microsoft Community Hub

Qtip: Connect Windows Azure VM to Azure SQL DB using Managed Identity

by Contributed | Jun 28, 2024 | Technology

This article is contributed. See the original author and article here.

In this guide I am going to show steps to connect Windows Azure VM to Azure SQL DB using Managed Identity covering create user in Azure SQL DB , connect using SSMS and connect using powershell

Requirements:

Windows 10 or 11 Azure Virtual Machine with system managed identity enabled and admin privileges to run powershell scripts
Azure SQL DB server with entra admin access and database for testing
SQL Server Management Studio (SSMS) latest version

Get required information from VM and managed identity:

Use Object (principal) ID to get Application ID

Go to Entra ID and search Object (principal) ID

Select result to get Application ID

Provide access to Azure SQL DB:

Connect to server/database using Entra user with admin privileges and create user in this case is the name of the computer

— DROP USER [managediddemo] –remove user if exists
CREATE USER [managediddemo] FROM EXTERNAL PROVIDER;
ALTER ROLE db_datareader ADD MEMBER [managediddemo];

Connect from Azure VM:

Connect using SQL Server Management Studio SSMS …

Open SSMS and provide server name , select authentication Microsoft Entra Managed Identity and user assigned Identity will be Application ID from VM

In connection properties provide database name otherwise you will receive an error if user is not administrator and finally connect

Now is connected

Connect using powershell…

To be able to connect using powershell you need to Install modules required for Azure

Open powershell as administrator and run commands below

Set-ExecutionPolicy unrestricted
Install-Module -Name PowerShellGet -Force 
Install-Module -Name Az -AllowClobber -Scope CurrentUser -force
Install-module SQLServer -force

Once modules are installed you can close powershell and open again as administrator

Get token

Connect-AzAccount -Identity 
$access_token = (Get-AzAccessToken -ResourceUrl https://database.windows.net).Token
write-host $access_token

*In some scenarios token string can be provided directly to avoid round trip each time

Test with invoke-sqlcmd

Connect-AzAccount -Identity
$access_token = (Get-AzAccessToken -ResourceUrl https://database.windows.net).Token
Invoke-Sqlcmd -ServerInstance .database.windows.net -Database  -AccessToken $access_token -query ‘select top 10 name from sys.tables’

 -query is the query to run in this case only gets a list of tables in database

Test using Microsoft.Data.SQLClient

import-module Az.Accounts
import-module Microsoft.PowerShell.Security
import-module Microsoft.WSMan.Management
import-module SqlServer 
$access_token = (Get-AzAccessToken -ResourceUrl https://database.windows.net).Token
$connectionstring=”Server=tcp:.database.windows.net,1433; Database=; Encrypt=True;”
$connection= New-Object Microsoft.Data.SqlClient.SqlConnection
# you can get connection string from azure portal in database overview
$connection.ConnectionString = $connectionstring
$connection.AccessToken=$access_token
$connection.Open()
$command= $connection.CreateCommand()
$command.CommandText = “select top 10 name from sys.tables”
$dataSet = New-Object system.Data.DataSet
$adapter = New-Object microsoft.Data.SqlClient.SqlDataAdapter $command
$adapter.Fill($dataSet) | Out-Null
$connectionid=$connection.clientconnectionid
write-output $connectionid
$dataSet.Tables

Now your Windows Azure VM is able to connect using different methods

More Information

Provision Azure AD admin (SQL Database)
https://docs.microsoft.com/en-us/azure/azure-sql/database/authentication-aad-configure?tabs=azure-powershell#provision-azure-ad-admin-sql-database

What are managed identities for Azure resources?

https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/overview

Configure managed identities on Azure virtual machines (VMs)

https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/how-to-configure-managed-identities?pivots=qs-configure-portal-windows-vm

Move to cloud authentication with the AD FS migration tool!

by Contributed | Jun 26, 2024 | Technology

This article is contributed. See the original author and article here.

We’re excited to announce that the migration tool for Active Directory Federation Service (AD FS) customers to move their apps to Microsoft Entra ID is now generally available! Customers can begin updating their identity management with more extensive monitoring and security infrastructure by quickly identifying which applications are capable of being migrated and assessing all their AD FS applications for compatibility. 

In November we announced AD FS Application Migration would be moving to public preview, and the response from our partners and customers has been overwhelmingly positive. For some, transitioning to cloud-based security is a daunting task, but the tool has proven to dramatically streamline the process of moving to Microsoft Entra ID. 

A simplified workflow, reduced need for manual intervention, and minimized downtime (for applications and end users) have reduced stress for hassle-free migrations. The tool not only checks the compatibility of your applications with Entra ID, but it can also suggest how to resolve any issues. It then monitors the migration progress and reflects the latest changes in your applications. Watch the demo to see the tool in action.

Moving from AD FS to a more agile and responsive, cloud-native solution helps overcome some of the inherent limitations of the old way of managing identities.

In addition to more robust security, organizations count greater visibility and control with a centralized, intuitive admin center and reduced server costs as transformative benefits of moving to a modern identity management. Moreover, Entra ID features can help organizations achieve better security and compliance with multifactor authentication (MFA) and conditional access policies—both of which provide a critical foundation for Zero Trust strategy.  

More Entra ID features include:

• Passwordless and MFA for better user experience.


• A rich set of apps, APIs, SDKs, and connectors for customization and extensibility.


• Granular adaptive access controls to define and monitor conditional access.


• Self-service portals that allow employees to securely manage their own identity.

Want to learn more about Microsoft Entra? Get the datasheet and take a tour here. Ready to get started? Visit Microsoft Learn and explore our detailed AD FS Application Migration guide. 

Have any questions or feedback? Let us know here.  

Melanie Maynes

Director of Product Marketing

For a comprehensive overview of the migration tool and its capabilities, check out these other resources:

• Overview of AD FS application migration – Microsoft Entra ID | Microsoft Learn


• Use the AD FS application migration to move AD FS apps to Microsoft Entra ID – Microsoft Entra ID | Microsoft Learn


• Demo: Effortless Application Migration Using Microsoft Entra ID | OD03 (youtube.com) 


• Best practices to migrate applications and authentication to Microsoft Entra ID – Microsoft Entra | Microsoft Learn


• Customer Case Study: Microsoft Customer Story-Universidad de Las Palmas de Gran Canaria boosts accessibility with Microsoft Entra ID  

Learn more about Microsoft Entra  

Prevent identity attacks, ensure least privilege access, unify access controls, and improve the experience for users with comprehensive identity and network access solutions across on-premises and clouds. 

• ⁠Microsoft Entra News and Insights | Microsoft Security Blog


• ⁠⁠Microsoft Entra blog | Tech Community


• ⁠Microsoft Entra documentation | Microsoft Learn


• Microsoft Entra discussions | Microsoft Community  

MGDC for SharePoint FAQ: How can I track the lifecycle of a SharePoint site?

by Contributed | Jun 24, 2024 | Technology

This article is contributed. See the original author and article here.

In this post, we’ll cover some details on how to track the lifecycle of a SharePoint Site in the Microsoft Graph Data Connect (MGDC), using the date columns in the SharePoint Site dataset. If you’re not familiar with MGDC for SharePoint, start with https://aka.ms/SharePointData. 

All Dates in the Sites Dataset

One of the most common scenarios in MGDC for SharePoint is tracking the lifecycle of a site, which includes understanding when the site was created, how it grows over time, when it stops growing and when it becomes inactive or abandoned.

The SharePoint Sites dataset includes several columns that can be used to understand the site lifecycle in general. For instance, here are the datetime columns available:

Site Created

Creation date is straightforward. There is a column (CreatedTime) with the date when the site was created. As with all other dates, it uses the UTC time zone.

Last Modified

In the Sites dataset, you also have the date and time when any items under the root web were last modified (RootWeb.LastItemModifiedDate). This includes the last time when files were created, updated or deleted. This is a great indication that the Site is still in active use.

You also have the date the site security was last modified (LastSecurityModifiedDate). This shows when permissions were granted, updated or revoked. That includes permissions granted through the manage access interface and permissions granted through sharing links.

Last Accessed

Last access is available at the site level (LastUserAccessDate). This shows when an item in the site was last accessed (this includes simply reading the file). This is an important indicator to help understand when the site is becoming inactive or abandoned.

Note that, while there is an effort to identify here only access performed directly by users, this date might also include automated actions by applications, including internal SharePoint applications.

Snapshot Date

Please note that there is one more date (SnapshotDate), but that one is not relevant to the site lifecycle. The snapshot date simple tracks when the data was retrieved by MGDC.

File Actions

Besides what’s captured in these datetime columns in the Sites dataset, you also have the option to capture detailed file activity in the site using the SharePoint File Actions and accumulate those over time.

Keep in mind that MGDC for SharePoint only keeps actions for the last 21 days due to compliance issues. More specifically, you can get file actions between today minus 2 days and today minus 23 days. For instance, if today is June 30^th, you can get file actions between June 8^th and June 28^th.

If you query this information daily, you could build a longer history of file actions over time. For instance, you could keep the last 90 days of data from the File Actions dataset. With that you could find recent access or otherwise say “no access in the last 90 days”.

This would also let you know more details about recent file activities, like who last accessed the site, which file or extension was last accessed, what was the last action, etc. You need to decide if you can rely solely on the date columns provided in the Sites dataset or if it is useful to keep these additional details.

Please do check with the compliance team in your company to make sure there are no restrictions on keeping this information for longer periods of time in your country. There might be regulatory restrictions on how long you can keep this type of personally identifiable information.

Calculated Columns

Keep in mind that these dates use a datetime data type, so grouping by one of them can sometimes be a challenge. If you’re using Power BI, you can show them as a date hierarchy and get a summary by year, quarter, month or day.

It might also be useful to create calculated columns to help with grouping and visualization. For instance, you can create a new date column (without the time portion) for daily summaries. Here’s how to calculate that in Power BI:

CreatedDay = DATE(YEAR(Sites[CreatedTime]),MONTH(Sites[CreatedTime]), DAY(Sites[CreatedTime]))

You could also create a column for monthly summaries like this:

CreatedMonth = FORMAT(Sites[CreatedTime], "yyyy-MM")

You can read more about calculated columns in Power BI at MGDC for SharePoint FAQ: How to create custom columns in Power BI.

Conclusion

I hope this clarifies what is available in MGDC for SharePoint to track the lifecycle of a SharePoint site.

Let us know in the comments if you think we should consider additional lifecycle information.

For further details about the schema of all SharePoint datasets in MGDC, including SharePoint Sites and SharePoint File Actions, see https://aka.ms/SharePointDatasets.

Exploring Generative AI: A Hands-on Course on Prompt Engineering for non-tech students – Part 2

by Contributed | Jun 22, 2024 | Technology

This article is contributed. See the original author and article here.

Introduction 

Welcome to Part 2 of our exploration into generative AI, where we delve deeper into the practical applications and creative potential of this innovative technology.

This article highlights concrete examples from students projects of the course ‘Prompt Engineering’ at Fondazione Bruno Kessler (FBK) in Trento (Italy). The aim is to  showcase how students leveraged generative AI in unique ways. In particular, we’ll focus on two fascinating projects: “Generative Music” and “Personal Chef,” which exemplify the versatility and impact of generative AI in diverse fields. 

Core element of these projects is the use of a structured framework known as the Card Model to define and organize generative AI tasks. In the context of this course, a card refers to a structured format or template used to define a specific task or objective for generating content or output using generative AI techniques. The Flow of these cards, meaning the logical sequence and interaction between them, is crucial for the coherent generation of complex outputs. For a detailed explanation of Card and Flow concepts read the 1st part of this blog series.

Our students have been actively experimenting with generative AI, producing remarkable results in their projects. Here, we present detailed insights and experiences from their hands-on work, demonstrating the practical applications of prompt engineering with non-tech students. 

Generative Music

The “Generative Music” project leverages generative AI technology to innovate the music creation process. Central to this project is the use of Generative AI Cards that define various musical parameters and guide the AI in generating unique compositions. Generative AI Cards specify key musical elements such as genre, number of chords, melody length, key, and instrumentation, including bass and guitar (Fig. 1). Each card represents a distinct aspect of the music, allowing for precise control over the generated content. By configuring these cards, the team can tailor the AI’s output to meet specific creative goals.

Card Configuration

The process begins with the selection and configuration of these cards. Initial configurations often require multiple iterations to achieve satisfactory results. Each card’s parameters are adjusted to optimize the music generation, focusing on refining the elements to create a harmonious and appealing output.

Fig 1: Example of Cards from the Music Project.

Flow Generation

Flow generation involves the structured combination of these AI Cards to produce a coherent piece of music. This stage is crucial as it dictates the sequence and interaction of different musical components defined by the cards. The project utilizes tools like Canva to aid in visualizing and organizing the flow of these components, ensuring a smooth and logical progression in the music. During the flow generation process, the order of the AI Cards is experimented to explore different musical outcomes. However, the team found that altering the sequence did not significantly affect the final output, indicating that the cards’ individual configurations are more critical than their order.

Iterative Refinement and Human Interaction

A significant aspect of the project is the iterative refinement process, where generated music undergoes multiple evaluations and adjustments. Human intervention is essential at this stage to validate the quality of the output. Listening to the music is the primary method for assessing its adequacy, as human judgment is necessary to determine whether the AI’s creation meets the desired standards. The team continuously modifies the prompts and configurations of the AI Cards based on feedback, refining the generative process to improve the music quality. This iterative cycle of generation, evaluation, and adjustment ensures that the final product aligns with the creative vision (Fig. 2).

Fig 2: genAI music showcase (youtube.com)

Lessons Learnt

The “Generative Music” project demonstrates the potential of generative AI in the field of music creation. By using Generative AI Cards and structured flow generation, the project showcases a methodical approach to producing unique musical compositions. Despite the need for substantial human involvement in the refinement process, this innovative use of AI represents a significant step forward in integrating technology with artistic creativity.

Personal Chef

The “Personal Chef” project utilizes generative AI to assist individuals in planning balanced meals efficiently. The primary goal of this project is to save time and resources, enhance creativity, and provide valuable insights for meal planning. Generative AI Cards are central to this project, serving as modular components that define specific meal planning parameters. Each card encapsulates different aspects of meal creation, such as the type of dish (e.g., balanced dish, vegetarian alternative), the ingredients required, and the nutritional composition (Fig. 3). These cards help in structuring the meal planning process by providing detailed instructions and alternatives based on user preferences and dietary needs.

Card Configuration 

For instance, one AI Card might focus on generating a list of high-protein foods, while another might ensure the meal components are seasonal. These cards are iteratively refined based on user feedback to ensure they deliver precise and relevant outputs. The language used in these cards is carefully chosen, as even small changes can significantly impact the results. The feedback loop is crucial here, as it allows continuous improvement and ensures that the AI provides more accurate and context-specific suggestions over time.

Fig 3: Example of Cards from the Personal Chef Project.

Flow Generation 

Flow generation in this project involves the logical sequencing and combination of Generative AI Cards to create coherent and balanced meal plans. This process ensures that the output not only meets nutritional guidelines but also aligns with the user’s preferences and constraints. The flow of these cards is designed to cover various stages of meal planning, from selecting ingredients to proposing complete dishes (Fig. 4). For example, a flow might start with an AI Card that provides a balanced dish recipe, followed by another card that suggests a vegetarian alternative, and then a card that customizes the dish based on seasonal ingredients. This structured approach ensures a logical progression and maintains the relevance and coherence of the meal plans.

Fig 4: Example of Flows from the Personal Chef Project.

Iterative Refinement and Human Interaction

The project emphasizes the importance of human feedback in refining the AI-generated outputs. Users can interact with the system to customize the generated meal plans, adding or removing ingredients as needed. This iterative process ensures that the AI’s suggestions remain practical and tailored to individual preferences and dietary requirements. By continuously incorporating user feedback, the project aims to enhance the precision and utility of the Generative AI Cards, ultimately making the meal planning process more efficient and enjoyable.

Lessons Learnt

The “Personal Chef” project showcases how generative AI can be leveraged to support everyday tasks like meal planning. The use of Generative AI Cards allows for a modular and flexible approach, enabling users to create personalized and balanced meal plans. While the AI can provide valuable insights and save time, human interaction remains essential to validate and refine the outputs, ensuring they meet the users’ specific needs and preferences. This integration of AI and human expertise represents a significant advancement in making daily routines more manageable and creative.

Students Survey Results

The class consisted of 11 students (average age 23, 5 female) from various university faculties, i.e. Psychology, Cognitive Science, Human-Computer Interaction. 

As said, it was a class of non-tech students. Indeed, most of them (7 out of 11) stated that they rarely (1-4 times in the last month) used tools such as ChatGPT. Only one student stated that he/she regularly (every day or almost every day) used these tools, either for study-related and unrelated purposes. One student admitted that he/she was not familiar with ChaptGPT, had only heard about it but had never used it.

In order to investigate the students’ knowledge of GenAI and its potential and to assess the effectiveness or otherwise of the course in increasing their knowledge and ability to use GenAI, we administered a questionnaire at the beginning and end of the course and then made comparisons.

The questionnaire consists of 50 items taken from existing surveys [1,2] investigating various dimensions concerning AI in general. These included: (a) AI Literacy, based on the level of knowledge, understanding and ability to use AI, (b) Anxiety, related to the fear of not being able to learn how to use AI correctly, as well as of losing one’s reasoning and control abilities, and (c) Self-Efficacy, related to confidence both in one’s technical capabilities and in AI as good aid to learning.

As evident from the graph below (Fig. 5), by comparing the answers given by the students before and after the course, it is evident that on average the students increased their literacy and self-efficacy, and decreased their anxiety. 

Fig. 5:. Average scores of students’ literacy, anxiety and self-efficacy gathered at the beginning and the end of the Prompt Engineering course for non-tech students.

Furthermore, at the end of the course we asked the students to answer 10 additional questions aimed at gathering their feedback specifically about Generative AI. In particular, we asked them to score the following statements using a 7-point Likert scale, where 1 means “strongly disagree” and 7 means “strongly agree”: 

Fig. 6 presents the average score and standard deviation of each item. As evident from the graph, after the course the students recognised the value of GenAI as a valuable tool to support learning (item 8). They also showed to be more aware of the possible uses of GenAI (item 3) and ethical implications of such uses (item 4), whereas they showed a low level of anxiety in attending the course (item 5) and a low level of fear of losing reasoning skills (item 6). 

Fig. 6: Average scores on a 7-point Likert scale given by non-tech students at the end of the Prompt Engineering course.

Conclusions

Generative AI is widely used in higher education and skills training. Articles like [3] demonstrate that Generative AI is widely used in higher education and skills training, highlighting its benefits for productivity and efficiency, alongside concerns about overdependence and superficial learning. In Part 2 of our blog series, we delved into the practical applications and creative potential of this innovative technology. Through projects like “Generative Music” and “Personal Chef,” our students demonstrated the versatility and impact of generative AI across diverse fields. Central to these projects was the structured framework known as the Card Model and a Flow of the identified cards, which helped define and organize generative AI tasks.

The course significantly enhanced students’ understanding of prompt engineering, reducing their anxiety and increasing their self-efficacy. Survey results indicated improved AI literacy and decreased anxiety, with students feeling more confident in their technical abilities and recognizing the value of generative AI as a learning tool. Utilizing atomic cards to define and organize generative AI tasks facilitated the learning process. This structured approach allowed students to better grasp and control various aspects of content generation. In the “Generative Music” and “Personal Chef” projects, the cards provided a flexible and modular framework, enabling iterative refinement and improved output quality.

Looking ahead, future developments could further enhance the effectiveness of teaching generative AI. Developing specific tools and editors for configuring prompts could simplify the process, making it more intuitive for students. Establishing standard guidelines and metrics for evaluating generative outputs could provide more structured feedback, improving the learning process. Additionally, expanding the course content to include a broader range of diverse and complex case studies could help students explore more generative AI applications, deepening their understanding and innovative capabilities.

These advancements would not only improve the teaching of generative AI but also promote greater integration of technology and creativity, better preparing students for their future professional career.

Antonio Bucchiarone

Motivational Digital System (MoDiS)

Fondazione Bruno Kessler (FBK), Trento – Italy

Nadia Mana

Intelligent Interfaces and Interaction (i3)

Fondazione Bruno Kessler (FBK), Trento – Italy

References

[1] Schiavo, Gianluca and Businaro, Stefano and Zancanaro, Massimo. Comprehension, Apprehension, and Acceptance: Understanding the Influence of Literacy and Anxiety on Acceptance of Artificial Intelligence. Available at SSRN: https://ssrn.com/abstract=4668256.

[2] Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: a perspective of AI anxiety. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2022.2153147

[3] Hadi Mogavi, Reza and Deng, Chao and Juho Kim, Justin and Zhou, Pengyuan and D. Kwon, Young and Hosny Saleh Metwally, Ahmed and Tlili, Ahmed and Bassanelli, Simone and Bucchiarone, Antonio and Gujar, Sujit and Nacke, Lennart E. and Hui, Pan. ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, Vol. 2, N. 1, 2024. https://doi.org/10.1016/j.chbah.2023.100027