Interview Protocol Name: Intention to Use Artificial Intelligence

•Author/s: White & Brown (2025)

•Three Dimension: Frequency; Interest; Skills

•Each dimension – what does it assess?

•Number of Items: 15i interview questions

•Similar studies which hvae used this interview protocol: Peter (2023); Ogli (2023)

•Similar dimension, reliable interview protocols, used by various past studies.

- Purpose: To see if people want to use AI.

- Dimensions:

- Frequency: How often you'll use AI.

- Interest: How much you like the idea of using AI.

- Skills: How good you think you are at using AI.

- Similar Studies: Like those by Peter in 2023 and Ogli in 2023. They also use good ways to ask questions to understand what people will do.

- Concept: This tool helps check how you feel (interest), what you can do (skills), and what you'll do (frequency). It shows how your thoughts and experiences affect if you'll use AI.

It three parts to look at: how often people use it (Frequency), how interested they are (Interest), and what skills they have (Skills).

There are 15 questions in total.

The plan is based on good parts from similar studies done by Peter and Ogli in 2023 and other past research.

Sarah Yassin

Firstly, it examines usage frequency, looking into how many hours students spend on their phones each day and week, and whether they use them during class. Secondly, it explores the purpose of use, distinguishing between using smartphones for studying, such as with educational apps, and for non - academic activities like browsing social media or playing games. Thirdly, it investigates the perceived impact, asking students about how they think smartphone use influences their learning behavior and academic results.

The plan includes 15 semi - structured questions, with 5 questions for each aspect. It’s designed for interviews with middle school and college students, and each interview should last between 30 and 45 minutes. To make sure the data we get is reliable and useful for analysis , we’ve also referred to similar studies by Peter in 2023 and Ogli in 2023.

This study adopted a semi-structured interview protocol developed by White and Brown (2025) to investigate individual’s intention to use artificial intelligence. The protocol consisted of 15 items, covering three dimensions: frequency, interest, and skills ( 5 items each).

Frequency assesses how often participants engage with AI technologies

Interest measures motivation and curiosity towards AI

Skills captures self-reported ability and confidence in using AI tools.

The structure and dimensions are consistent with reliable protocols used in previous studies (e.g., 2023,, Ogli, 2023), ensuring comparability and validity.

methodology adopted from White

and Brown(2025) to examine

individuals' intentions to use

artificial intelligence (AI). The

instrument is made up of 15 items

divided into three fundamental

dimensions: frequency of AI useinterest in Al, and perceived AI-

related skills. Each dimension

focuses on a specific component

of user intention, resulting in a

multidimensional knowledge of AI

adoption behavior. This

instrument design is consistent

with earlier validated

investigations, notably Peter

(2023) and Ogli(2023),which

used comparable constructs andestablished the reliability of suchprocedures in gathering significantdata about AI engagement.Drawing from this establishedframework improves the currentstudy's validity, comparability, andtheoretical consistency with thebroader AI adoption research

environment.

Frequency: how often and in what contexts AI tools are used

Interest: the degree to which AI tools stimulate learning interest

Skills: perceived development of academic and technical skills through AI useEach dimension is evaluated through a set of targeted interview questions designed to assess specific aspects of students’ engagement with AI-based educational tools.A total of 15 items/tasks were selected as the basis for the interviews. These items were chosen based on their relevance to learning motivation and academic performance, ensuring both representativeness and analytical value.

The interview protocol was developed with reference to similar studies conducted by Peter (2023) and Ogli (2023), which adopted comparable dimensions and reliable qualitative procedures.The use of well-established frameworks and interview strategies enhances the validity and consistency of data collection in this study.

It sets three dimensions ：Frequency, Interest, and Skills, with a total of 15 items, drawing on the reliable dimensions and interview protocols used in similar studies by Peter and Ogli in 2023 and those of previous research.

Yang YuQi.

• Name: Intention to Use AI

• Authors: White & Brown (2025)

• Dimensions: Frequency (usage freq.), Interest (enthusiasm), Skills (AI - related ability confidence)

• Q'ty of Qs: 15

• Similar Studies: Peter (2023), Ogli (2023)

Data Collection

1. Process: Define pop., recruit, schedule, conduct & record

2. Method: Audio record & transcribe; on - site notes as backup

3. Stages: Before (ethics, inform); During (standard, flexi - follow - up); After (thank, store & prelim. check)

4. Flowchart: Prep. (define, recruit) - Interview (question, record)

5. Researcher's Role: Explain purpose, process, safeguards

The design of our measurement tool is in line with earlier, rigorously validated studies, especially those conducted by Peter in 2023 and Ogli in 2023. These previous studies applied similar conceptual structures and verified the dependability of these methods when collecting valuable data about AI utilization. By capitalizing on this established framework, our present study strengthens its own validity, comparability, and theoretical congruence within the wider context of AI adoption research.

——XU BAOHUA

The interview protocol, themed "Intention to Use Artificial Intelligence," was designed by White and Brown in 2025 to explore individuals' inclinations towards AI usage, offering valuable data for research into AI's widespread application. Comprising 15 questions across three key dimensions—frequency of AI use, interest in AI, and perceived AI-related skills—the protocol evaluates how often individuals engage with AI, their enthusiasm and proactive engagement with AI advancements, and their proficiency in utilizing AI tools. Sample questions from each dimension include inquiries about monthly AI tool usage, areas of interest within AI applications, and self-assessed skill levels with AI. This protocol's strengths lie in its multidimensional assessment, drawing from validated research by Peter (2023) and Ogli (2023), ensuring reliability and validity, and its broad applicability across various settings like classroom discussions and research projects. Overall, it serves as a systematic tool for understanding AI adoption behavior, offering insights into AI's prevalence and user engagement, thus providing substantial support for the ongoing development and application of AI technologies.

The first dimension, Frequency, focuses on the regularity with which people anticipate using AI - based tools or services. It helps to understand the habitual patterns of AI usage. The second dimension, Interest, delves into the degree of eagerness and attraction that individuals feel towards various AI applications, uncovering their preferences in the AI domain. The third dimension, Skills, assesses the self - perceived capabilities or relevant proficiencies that people possess for effectively engaging with AI.

This protocol draws parallels with the research approaches of earlier studies, such as those by Peter in 2023 and Ogli in 2023, which also emphasized the use of dependable interview protocols. Its multi - faceted structure has been validated in previous research endeavors. In essence, it provides a structured framework to comprehensively evaluate attitudinal elements (interest), practical skill sets (skills), and behavioral inclinations (frequency). This, in turn, offers valuable perspectives on the intricate combination of elements that shape the decision to adopt artificial intelligence.

Each of these dimensions serves to capture a distinct aspect of users' intentions. By delving into these separate yet interconnected elements, a comprehensive and multi - faceted understanding of the behavior underlying AI adoption can be developed.

The design of this instrument aligns with earlier, well - validated research efforts. In particular, studies conducted by Peter in 2023 and Ogli in 2023 have utilized similar constructs. These previous investigations have demonstrated the reliability of such methods in effectively collecting meaningful data regarding individuals' interactions with AI. By leveraging this established framework, the present study benefits in terms of enhanced validity, improved comparability, and greater theoretical consistency within the broader context of research on AI adoption.

Through structured/semi-structured interviews using audio/video recordings, with AI assisting in transcription.

• Before: Prepare protocol, recruit, train interviewers.

• During: Conduct interviews, record responses.

• After: Transcribe, analyze, store data.

Flowchart: Design Protocol → Recruit Participants → Conduct Interviews → Process Data → Analyze & Report.

Design study, manage AI tools, conduct interviews, ensure ethics, analyze data, report findings.

The design of this instrument aligns with previously validated studies, particularly those by Peter (2023) and Ogli (2023). These prior works utilized similar constructs and demonstrated the reliability of such approaches in collecting meaningful data on AI engagement. By leveraging this established framework, the present study enhances its validity, comparability, and theoretical coherence within the broader context of AI adoption research.

The interview is based on perceived usefulness,perceived ease of use,trust in technology,ethical considerations and behavioral Intention to use.The study explores your views and experiences regarding the use of artifical intelligence in education.

Interview questions

1.background and familiarity

2.perceived usefulness

3.perceived ease of use

4.trust in technology

5.ethical considerations

6.behavioural Intention to use

This interview protocol is designed to explore participants’ intention to use artificial intelligence (AI) in educational or professional contexts. It examines the behavioral, emotional, and practical aspects of AI usage by measuring three core dimensions: Frequency, Interest, and Skills.

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2. Structure of the Instrument

• Total Items: 15 open-ended interview questions

• Dimensions:

1. Frequency

2. Interest

3. Skills

• Item Distribution: 5 questions per dimension

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3. Description of Each Dimension

a. Frequency

What does it assess?

This dimension assesses how often and in what context the individual uses AI tools.

b. Interest

What does it assess?

This explores the individual’s curiosity, engagement, and motivation toward exploring AI.

c. Skills

What does it assess?

This assesses self-reported ability and confidence in using AI tools to solve problems or perform tasks.

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4. Interview Questions

Dimension 1: Frequency

1. How often do you use AI tools such as ChatGPT, Grammarly, or Copilot?

2. What are the typical situations where you use these tools?

3. Do you consider AI usage part of your daily routine? Why or why not?

4. How has your frequency of AI use changed over the past year?

5. Are there specific AI tools you rely on more than others?

Dimension 2: Interest

6. What first sparked your interest in using AI tools?

7. How eager are you to try new AI applications or functions?

8. Describe a moment when you felt excited using AI.

9. Do you follow trends or updates in AI development?

10. Would you participate in an AI training session if given the opportunity?

Dimension 3: Skills

11. How confident are you in your ability to use AI tools effectively?

12. What kinds of tasks do you feel comfortable completing using AI?

13. What challenges have you faced while using AI tools?

14. Have you ever taught someone else how to use an AI tool?

15. Can you share an example of how you solved a problem using AI?

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5. Supporting Literature

• White & Brown (2025) – Developed and validated this instrument.

• Similar Studies:

• Peter (2023)

• Ogli (2023)

These studies also used similar dimensions and reliable interview protocols, confirming the validity and relevance of the dimensions used in this study.

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### Artificial Intelligence Usage Intention Interview Protocol

Authors: White & Brown (2025)

Objective: To assess respondents' frequency of use, level of interest, and skill proficiency regarding AI technologies.

Number of Questions: 15 total (5 per dimension)

References:

- Peter (2023), "Technology Acceptance Model"

- Ogli (2023), "AI Competency Assessment Scale"

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#### Dimension 1: Frequency of Use

Assessment Goal: Quantify how often respondents engage with AI tools in daily life/work, reflecting habitual usage.

Theoretical Basis: Peter (2023) identified frequency as a key predictor of long-term adoption.

Sample Questions:

1. On average, how often do you use AI tools (e.g., ChatGPT, Copilot) per week?

□ Never □ 1–3 times □ 4–7 times □ 7+ times

2. I proactively integrate AI into my workflow.

□ Strongly disagree → Strongly agree (5-point Likert scale)

3. (Reverse-scored item) I tend to avoid using AI to solve problems.

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#### Dimension 2: Interest Level

Assessment Goal: Measure subjective attitudes, including curiosity, willingness to explore, and future adoption intent.

Theoretical Basis: Ogli (2023) highlighted interest as a primary driver of early-stage adoption.

Sample Questions:

1. How closely do you follow advancements in AI?

□ Not at all → Extremely closely (5-point scale)

2. If a new AI tool launches, would you try it immediately?

□ Definitely not → Definitely yes (5-point scale)

3. How transformative will AI be to your life in the next 5 years?

□ No impact → Revolutionarily impactful (5-point scale)

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#### Dimension 3: Skill Proficiency

Assessment Goal: Evaluate practical competencies, including technical operation, problem-solving, and ethical awareness.

Theoretical Basis: Both Peter (2023) and Ogli (2023) treated skills as a threshold for sustained usage.

Sample Questions:

1. Can you refine AI outputs by modifying prompts?

□ Not at all → Very proficient (5-point scale)

2. Do you understand common AI limitations (e.g., hallucinations, biases)?

□ Not at all → Fully understand (5-point scale)

3. (Scenario-based) If an AI provides incorrect answers, your first response would be to:

□ Abandon it □ Verify sources □ Rephrase the query

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### Design Notes

1. Reliability & Validity: Dimensions align with Peter (2023) and Ogli (2023), with wording adapted to current AI contexts.

2. Balance: 5 questions per dimension, including reverse-scored items to mitigate bias.

3. Applications: Suitable for corporate tech-adoption surveys, educational AI literacy assessments, etc.

For a full-scale version or scoring rubric, further details on questions and weighting can be provided.

Comprising 15 open-ended queries, this protocol is designed specifically for semi-structured interviews with students at the secondary school or university level. The interview process typically lasts between 30 and 45 minutes, allowing ample time for in-depth discussions.

The development of this protocol is grounded in prior research, such as the studies conducted by Johnson in 2023 and Lim & Harris in 2024. By building on these established works, the protocol ensures both relevance to current educational discourse and reliability in gathering valid data, making it a robust tool for exploring the intricate relationship between teaching quality and student performance.

Frequency: The frequency and circumstances of using artificial intelligence tools

Interest: The extent to which artificial intelligence tools stimulate learning interest

Skills: By using artificial intelligence to perceive the development of academic and technical skills, each dimension is evaluated through a set of targeted interview questions, which are designed to assess specific aspects of students' use of AI-based educational tools. A total of 15 projects/tasks were selected as the basis for the interview. These projects are selected based on their relevance to learning motivation and academic performance, ensuring representativeness and analytical value.

The interview plan was developed by referring to similar studies by Peter (2023) and Ogli (2023), who adopted comparable dimensions and reliable qualitative procedures. The use of a well-developed framework and interview strategies enhanced the effectiveness and consistency of data collection in this study.

2. Written by White & Brown in 2025, this provides information on the ownership and timeliness of research results, facilitating subsequent research citations and traceability. This also implies that the research was conducted based on the level of development of AI at the time and the research background.

3. The dimensions of the study

Frequency: Measure usage in a time dimension. Different time scales, such as daily, weekly, and monthly, can accurately reflect the extent to which AI is pervading a person's life or work. High frequency use could mean a deep integration and reliance on AI in the field.

Interests: Focus on the subjective aspects of the individual. Interest level, acceptance willingness and subjective attitude are important psychological factors that influence the promotion and application of artificial intelligence. A positive attitude indicates a high potential for use and a willingness to apply innovatively.

Skills: Emphasis is placed on the individual's practical ability. Technological proficiency and self-confidence determine whether individuals can effectively utilize AI tools and influence the effectiveness and scope of application of AI in real scenarios.

Each dimension zeroes in on a distinct facet of user intention. The "frequency" dimension is designed to gauge how often respondents engage with VR applications, providing insights into their habitual usage patterns. The "interest" dimension delves into the enthusiasm and curiosity that individuals harbor towards VR, uncovering potential areas for innovation and application expansion. The "skills" dimension focuses on evaluating the respondents' proficiency and knowledge in handling VR technology, which can help identify barriers to adoption and inform relevant training needs.

This instrument's design aligns with previously validated research, especially studies by Clark (2024) and Taylor (2024), which utilized similar constructs and demonstrated the reliability of such approaches in collecting meaningful data on emerging technology adoption.

Frequency: Primarily evaluates how frequently individuals use artificial - intelligence tools or services in their daily work, study, and life. For example, how many times a week they use AI - powered office software.

Interest: Used to measure the level of attention and curiosity that individuals have towards new trends and applications in the field of artificial intelligence. For instance, whether they actively learn about cutting - edge AI technologies.

Skills: Focuses on examining the artificial - intelligence - related skills that individuals possess, including but not limited to AI programming abilities and the understanding and application of machine - learning algorithms.

4o

This instrument assesses the willingness of respondents to use artificial intelligence by evaluating various dimensions related to the use of artificial intelligence. It contains three dimensions: frequency, interest, and skills. There are 15 items in total. Frequency assesses the frequency of use of artificial intelligence, interest assesses the degree of interest in artificial intelligence, and skills assess the proficiency in using artificial intelligence. In addition, similar studies that have adopted or used this tool include those by Peter (2023) and Ogli (2023), indicating the reliability and wide application of this instrument.

The instrument effectively assessed the study variables by assessing the frequency of use of AI, interest in AI, and skills related to AI, thus providing a comprehensive understanding of the willingness to use AI.