Today, artificial intelligence (AI) is increasingly being integrated at all levels of education, especially at higher education level. The necessity of AI-supported technology in education is underlined, and it is stated that AI integration has become essential for effective information presentation and planning (Mallik & Gangopadhyay, 2023). This integration of AI into educational environments has the potential to lead to many important innovations in the way students learn, lecturers' learning-teaching processes and universities’ function (Ahmad et al., 2022).

It is emphasized that AI offers a highly significant development and unprecedented qualitative improvement (Ocaña-Fernández, Valenzuela-Fernández, & Garro-Aburto, 2019) for all levels of education, such as technological advances in education, theoretical innovations and successful pedagogical impact (Roll & Wylie, 2016) and providing innovative teaching and learning solutions (Bearman & Ajjawi, 2023). As an effective example, AI facilitates the preparation of educational materials in many areas, from content generation to customized learning experiences. AI-supported instructional design processes have the potential to enrich student-centered learning experiences while relieving the burden of instructional designers in the content creation process (Hwang, et al., 2020). Innovative solutions offered by AI can accelerate instructional design processes and support the development of effective instructional materials. However, these positive and facilitating aspects of AI technologies are directly related to instructional designers’ perception, adoption and utilization, their competences and experiences in this field.

One of the most important needs and focal points of higher education institutions today is to train professionals who implement programs developed with the support of AI and have the ability to plan, design, develop and implement learning processes accordingly (Popenici, & Kerr, 2017). How instructional designers perceive AI tools, in which processes they use these technologies effectively, and the challenges they face are among the issues that have not yet been sufficiently researched (Chen et al., 2022). In addition, more research is needed on how to adapt AI applications to learning environments in higher education, how to create intelligent learning and teaching systems, and how to take advantage of opportunities (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019). In this sense, this research aims to determine instructional designers' perspectives, experiences, and potentials for implementing the process of AI-supported material development. In line with this general aim, answers have been sought to the following research questions:

1. How do instructional designers perceive the process of AI-supported material development?
2. What are the advantages and limitations of AI-supported material development process?
3. At what level and for what purposes do instructional designers use AI tools?
4. What are the main difficulties encountered in the process of AI-supported material development?
5. What are the pedagogical, technical and ethical implications of AI-supported material development process in instructional design?
6. What are the suggestions of instructional designers for more effective implementation of AI-supported material development process?
7. What kind of support mechanisms or training programs do they need to make the AI-supported material development process more effective?

The study is designed in a mixed research design. Mixed methods research involves researchers’ combining elements of qualitative and quantitative research approaches for the purpose of breadth and depth of understanding and verification. (Johnson et al., 2007). Firstly, quantitative data will be used to determine the current situations of instructional designers on AI-supported material development with a questionnaire consisting of Likert-type questions developed by the researchers as a result of validity and reliability analyses. Secondly, qualitative data will be collected with semi-structured interview forms consisting of open-ended questions developed and analyzed in terms of validity and reliability by the researchers. It is aimed to collect quantitative and qualitative data independently, analyze them and interpret them comparatively in the final stage of the study. In this direction, convergent parallel mixed approach is preferred in the study. Because convergent parallel design requires the researcher to use quantitative and qualitative elements in the research process, to give equal weight to the methods, to analyze the two components independently and to interpret the results together (Creswell & Plano Clark, 2011). The questionnaire to be used in the first stage of the study will be applied online through Google Forms to instructional designers working at different faculties and colleges in higher education institutions through purposive sampling. In the second stage, among the instructional designers who filled out the questionnaire, instructional designers working in different fields with different levels of experience in terms of developing AI-supported materials will be selected in line with maximum diversity sampling. Face-to-face or online interviews will be conducted with these participants. With the permission of the participants, the interviews will be recorded, transcribed and analyzed. In the analysis of the quantitative data obtained within the scope of the study, descriptive statistical analyses will be conducted for the participant profile and the participants’ current situation on the use of AI using SPSS 26.0 software. In the analysis of qualitative data, common themes emerging from the interviews will be identified and reported using thematic analysis (Braun & Clarke, 2006). The coding process will be carried out separately by two researchers and inter-coder reliability will be calculated. In the first stage of coding, open coding will be used, and then common themes determined by the researchers will be formed. The results obtained from the study will be presented in an integrated manner as both quantitative and in-depth narrative.

This study is expected to make significant contributions to understanding the experiences and perceptions of instructional designers towards AI-supported material development processes. It will reveal the advantages, difficulties and support mechanisms needed by instructional designers in the process of AI-supported material development. In this context, investigating the experiences and perspectives on the integration of AI into instructional design processes is of critical importance to understand the dynamics of the digital transformation in education. In the study, the current status of instructional designers in developing AI-supported materials will be revealed. In addition, instructional designers are expected to describe their experiences in the process of AI-supported material development, the outcomes, advantages and opportunities provided by the process. On the other hand, their negative experiences such as difficulties and concerns about the process of developing AI-supported materials will also be revealed. In addition, as a result of the study, the need for support mechanisms and training programs for instructional designers on issues such as technical competence, ethical concerns and data security may be among the possible results of the study. In particular, it is expected that different views will emerge on how the integration of AI tools into the instructional design process has an impact on pedagogical approaches and student-centered learning processes. As a result, the study is expected to provide suggestions for future training programs and support mechanisms by revealing the difficulties, advantages and limitations encountered by instructional designers in AI-supported instructional material development processes. The findings obtained can be a valuable guide for academicians, instructional designers and policy makers in the field of educational technologies.

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