Types Of Questions Research Articles

Quality of Information Provided by Artificial Intelligence Chatbots Surrounding the Reconstructive Surgery for Head and Neck Cancer: A Comparative Analysis Between ChatGPT4 and Claude2.

Artificial Intelligences (AIs) are changing the way information is accessed and consumed globally. This study aims to evaluate the information quality provided by AIs ChatGPT4 and Claude2 concerning reconstructive surgery for head and neck cancer. Thirty questions on reconstructive surgery for head and neck cancer were directed to both AIs and 16 head and neck surgeons assessed the responses using the QAMAI questionnaire. A 5-point Likert scale was used to assess accuracy, clarity, relevance, completeness, sources, and usefulness. Questions were categorised into those suitable for patients (group 1) and those for surgeons (group 2). AI responses were compared using t-Student and McNemar tests. Surgeon score agreement was measured with intraclass correlation coefficient, and readability was assessed with Flesch-Kincaid Grade Level (FKGL). ChatGPT4 and Claude2 had similar overall mean scores of accuracy, clarity, relevance, completeness and usefulness, while Claude2 outperformed ChatGPT4 in sources (110.0 vs. 92.1, p < 0.001). Considering the group 2, Claude2 showed significantly lower accuracy and completeness scores compared to ChatGPT4 (p = 0.003 and p = 0.002, respectively). Regarding readability, ChatGPT4 presented lower complexity than Claude2 (FKGL mean score 4.57 vs. 6.05, p < 0.001) requiring an easy-fairly easy English in 93% of cases. Our findings indicate that neither chatbot exhibits a decisive superiority in all aspects. Nonetheless, ChatGPT4 demonstrates greater accuracy and comprehensiveness for specific types of questions and the simpler language used may aid patient inquiries. However, many evaluators disagree with chatbot information, highlighting that AI systems cannot serve as a substitute for advice from medical professionals.

Social attention in the wild - fixations to the eyes and autistic traits during a naturalistic interaction in a healthy sample

Attention to social stimuli is a key component of social behavior and facilitates the development of fundamental social skills. Studies investigating social attention in neurotypical or neurodiverse populations have often relied on screen-based experiments using static images or videos, which lack the sensory richness and reciprocity present in real-life social interactions. This can possibly be attributed to the challenges one encounters when creating naturalistic experiments, such as dealing with dynamically moving areas of interest (AOIs), which require either time-intensive manual coding or restraining of participants. Here, we present findings from an experimental paradigm using unrestrained mobile eye-tracking and a face detection algorithm (MTCNN) to measure fixation rates during a semi-structured, face-to-face interview. Data from N = 62 healthy adult participants was analyzed for gaze behavior and related to participants’ autistic traits. We observed a significant negative correlation between fixation rates on the eye region averaged over the entire interaction and scores on the autism spectrum quotient (AQ) (r = −0.14), indicating participants with high autistic traits fixated less frequently on the eye region. We also compared different types of interview questions (open vs. closed) to explore whether the reduction in fixation rates was more pronounced for specific time intervals during the interview. Lastly, we discuss both possibilities for extensions as well as limitations of the presented paradigm that could serve as inspiration for future research.

Gender Differences and Similarities in High School Science Performance— What Do Item Response Patterns Tell Us?

ABSTRACT The current study expands on previous research on gender differences and similarities in science test scores. Using three different approaches – differential item functioning, differential distractor functioning, and decision tree analysis – we examine a high school science assessment administered to 3,849 10th-12th graders, of whom 2,021 are girls. Our findings confirm no significant gender differences in overall science performance. However, we identify differences in the incorrect distractor responses between females and males, especially on items that require spatial thinking. This suggests that while males and females perform similarly on this science assessment, they may approach certain types of questions in different ways. Our study provides a nuanced investigation of gender differences and similarities in science performance. It highlights the importance of considering specific aspects of science assessments, such as spatial thinking, when examining performance patterns between males and females.

Interviewing Asylum-Seeking Children: A Scoping Review of Research to Inform Best Practices.

Immigration interviews with asylum-seeking youth have been largely understudied. In domestic legal settings, children interviewed about abuse and maltreatment provide more detailed, relevant responses when asked open-ended questions and when interviewed in a neutral environment, among other supportive practices. In asylum settings, guidance for interviews with youth derives from the United Nations Convention on the Rights of the Child. It is not clear to what extent best practices are employed during asylum interviews with youth. This scoping review was performed to (a) provide an overview of empirical literature on interviews with children in immigration settings, including border screenings, interviews with representatives, and asylum hearings, (b) explore whether best practices derived from forensic psychology and children's rights are observed in asylum interviews, (c) identify unique interview needs of asylum-seeking youth, and (d) derive implications for research and practice. A scoping review of three databases conducted in October 2023 yielded titles, of which 29 articles met inclusion criteria. These comprised quantitative and qualitative studies in English from 2003 to 2023. Three articles identified were quantitative, and 26 were qualitative. While several articles touched on interview practices and youth's experiences of interviews, only a few examined how asylum-seeking youth responded to different interview factors such as question type and interview setting. Key findings highlight inconsistent application of best practice principles, and several areas where best practices to support asylum-seeking children require clarification through further research.

Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation.

Visual Question Answering on 3D Point Cloud (VQA-3D) is an emerging yet challenging field that aims at answering various types of textual questions given an entire point cloud scene. To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8,771 3D scenes. Specifically, we develop a question engine leveraging 3D scene graph structures to generate diverse reasoning questions, covering the questions of objects' attributes (i.e., size, color, and material) and their spatial relationships. Through such a manner, we initially generated 44K questions from 1,333 real-world scenes. Moreover, a more challenging setup is proposed to remove the confounding bias and adjust the context from a common-sense layout. Such a setup requires the network to achieve comprehensive visual understanding when the 3D scene is different from the general co-occurrence context (e.g., chairs always exist with tables). To this end, we further introduce the compositional scene manipulation strategy and generate 127K questions from 7,438 augmented 3D scenes, which can improve VQA-3D models for real-world comprehension. Built upon the proposed dataset, we baseline several VQA-3D models, where experimental results verify that the CLEVR3D can significantly boost other 3D scene understanding tasks.

Evaluation of 6th and 7th Grade Mathematics Course Common Written Exam Questions According to Item Types and Revised Bloom's Taxonomy Prepared by Kocaeli Provincial Directorate of National Education Measurement and Evaluation Center

This study aims to evaluate the 6th and 7th grades mathematics lesson common written exam questions prepared by Kocaeli Provincial Directorate of National Education Measurement and Evaluation Center according to item types and the Revised Bloom Taxonomy. In this research, descriptive scanning method was used. In the research, as a data collection tool, the first and second semesters of 2018-2019 academic year and the first semester of 2019-2020 academic year’s exam questions of mathematics lesson were put to use by Kocaeli Provincial Directorate of National Education’s Measurement and Evaluation. The common written 195 exam questions, were examined in agreement with the item types and Revised Bloom Taxonomy. Obtained data show %2,88 of 6th- grathe questions were prepared for memorization, % 33,65 for comprehension, % 44,2 for exercise, % 8,65 for analysis, %3,85 for evaluation. It was determined that %37,5 of questions written in mathematics exam are true-false, are short-answer, % 59.62 are multiple-choice, and it is confirmed that there are also no questions from matched and open-ended question types.. According to the Revised Bloom Taxonomy, % 9,62 of e were no questions which test the creativity of the students. Besides, % 45,05 of 7th- grade mathematics written questions consisted of short answers, and % 54,95 consisted of multiple-choice items. It was observed that there were no open-ended, matched and true-false questions. As stated in Revised Bloom Taxonomy, % 2,20 of the questions were prepared for memorization, % 43,95 for comprehension, % 51,65 for exercise, %2,20 for analysis.

A Shift from Teacher-centric Method to Inquiry-based Coaching in Learning English as a Second Language

For more than two years, the Covid-19 pandemic hit the country’s education and academic set-up had to change overnight with the sudden lockdown imposed not only all over the country but of the world. With a dynamic shift from physically going to educational institutions to attending classes virtually, students and teachers faced a tough time with virtual trends in education. Shifting from teacher-centric approach to a more unconventional way of teaching made it difficult for everyone to adopt and adapt (Rodricks, 2021). While jumping into a new mode of teaching with almost no preparation seemed not easy but teachers did not back down. The educators made it a point to ensure no student was left behind, came up with methods to teach and retain students’ attention. Whether teaching was in school, teaching from home, or adopting a blended approach, teachers have had commitment to helping the learners. This sudden transition had occurred from face-to-face to online teaching. With online classes teachers had learned to accommodate the needs of students to create an effective teaching environment to keep learning in sync with the changing times (Kim 2020). The researcher would like to find out in this study whether this shift from teacher-centric method had changed the learning of a second language using the inquiry-based coaching in general and in specific study the kinds of questions students asked and how these queries were responded. Using modified questionnaires, the researcher surveyed 150 students to assess not only whether the opportunity given to them to inquire was sufficient but also the frequency of their asking of questions. For teachers, the study hoped to reveal the types of questions they asked in the class. Using discourse analysis, the researcher was able to examine the ways in which language was used in the classroom. The investigated data revealed a merged of linguistics and sociolinguistics cultural contexts to better understand an aspect in communication. Highlight of the study was to evaluate the possible twist of instruction from teacher-centric method to inquiry-based approach in ESL learning, where students inquire instead of the teachers asking questions.

Automatic distractor generation in multiple-choice questions: a systematic literature review

Background Multiple-choice questions (MCQs) are one of the most used assessment formats. However, creating MCQs is a challenging task, particularly when formulating the distractor. Numerous studies have proposed automatic distractor generation. However, there has been no literature review to summarize and present the current state of research in this field. This study aims to perform a systematic literature review to identify trends and the state of the art of automatic distractor generation studies. Methodology We conducted a systematic literature following the Kitchenham framework. The relevant literature was retrieved from the ACM Digital Library, IEEE Xplore, Science Direct, and Scopus databases. Results A total of 60 relevant studies from 2009 to 2024 were identified and extracted to answer three research questions regarding the data sources, methods, types of questions, evaluation, languages, and domains used in the automatic distractor generation research. The results of the study indicated that automatic distractor generation has been growing with improvement and expansion in many aspects. Furthermore, trends and the state of the art in this topic were observed. Conclusions Nevertheless, we identified potential research gaps, including the need to explore further data sources, methods, languages, and domains. This study can serve as a reference for future studies proposing research within the field of automatic distractor generation.

Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.

Spatial transcriptomics methods provide insight into the cellular heterogeneity and spatial architecture of complex, multicellular systems. Combining molecular and spatial information provides important clues to study tissue architecture in development and disease. Here, we present a comprehensive do-it-yourself (DIY) guide to perform such experiments at reduced costs leveraging open-source approaches. This guide spans the entire life cycle of a project, from its initial definition to experimental choices, wet lab approaches, instrumentation, and analysis. As a concrete example, we focus on tertiary lymphoid structures (TLS), which we use to develop typical questions that can be addressed by these approaches.

Filtered products of injective objects in Grothendieck categories

The transfer of properties from a family of modules to their direct product or to their direct sum has always been a topical subject of great interest. In this paper we address this type of questions in the general framework of (locally finitely generated) Grothendieck categories and for more general constructions that leave as particular cases direct sums or products: F -products, F being a filter on the chosen index set. Thus, we study the conditions on the categories for the F -products of any family of (M-)injective objects, or else of any family of copies of a single injective object, to be injective.

Generative AI

Generative AI is developing at an exponential pace, showing immense potential for reshaping teaching and learning methodologies. Designing high-quality examinations demands careful consideration, such as incorporating diverse question types to assess different levels of understanding and ensuring alignment with learning outcomes. However, many university educators encounter challenges in designing effective examinations due to insufficient training. This paper outlines our work-in-progress in leveraging generative AI, specifically large language models, as a copilot in examination design. In this study, we are evaluating large language models’ performance in assessing examination questions based on four key quality criteria. Preliminary findings indicate that ChatGPT 3.5 achieved comparable performance to the state-of-the-art in classifying questions by cognitive complexity, as defined by Bloom’s taxonomy. Additionally, ChatGPT 3.5 demonstrated promising initial results across other criteria. In conclusion, generative AI shows substantial potential to serve as a valuable tool in assisting university educators in enhancing the overall quality of examination design.

Trainee pharmacists’ evaluation of the formal learning elements within the N. Ireland foundation training year

Abstract Introduction Foundation Training Year (FTY) is a 12-month programme which prepares trainee pharmacists (TPs) to develop the skillset of a registered pharmacist and aids preparation for the Common Registration Assessment (CRA) via both work-based learning and formal learning. The formal learning included within the NI FTY programme consists of development day sessions with associated pre-requisite materials, including exam-style questions. The TPs attend 13 development days which are focused on high and medium weighted topics within the Registration Assessment Framework1 . The sessions take place online and the format is that of case-based discussion and exam-style questions, as well as two mock exams. Aim The aim of this study was to analyse TPs’ evaluation of how the formal learning elements prepared them for the CRA. Methods TPs (n=128) within FTY 2022/2023 completed an end-of-year evaluation relating to the whole programme, including the formal learning elements. 128 valid responses were received. The evaluation took the form of an online questionnaire with various question types, from Likert scale style(1-9) to free-text responses and was administered via an online link emailed to TPs. No ethical approval was required as the evaluation was not a component of a research project. Results In relation to the formal learning elements of the programme, 61% of trainee pharmacists rated the pre-requisite materials for the development day sessions at 6 and above whilst 57% of respondents rated the development day sessions at this level. An increased number of respondents (86%) rated the practice exam-style questions accessed via Moodle at 6 and above with the vast majority (99%) strongly agreeing/agreeing that regular exposure to exam style questions helped them to learn on an ongoing basis, particularly for high and medium weighted registration assessment topics. TPs valued the opportunity to attempt mock exams, with 99% strongly agreeing/agreeing that the mocks prepared them to sit the CRA and helped them to identify developmental needs. Discussion / conclusion The majority of TPs valued the formal learning elements of the FTY and felt the sessions and mock exams prepared them to sit the CRA. TP experience of exposure to exam questions was overwhelmingly positive with TPs acknowledging the importance of regular exposure to this element which allows them to develop their knowledge and become proficient in answering the types of questions asked in the CRA. This provides support for the inclusion of practice exam-style questions on a routine basis in the formal learning programme to support the FTY. Limitations to the study are that TPs completed the survey prior to obtaining results for the CRA, which may have influenced their responses to the questions posed, however the N. Ireland results were higher than the national average, with 95.6% first time sitters passing the exam in June 2023. Given this data, we changed formal learning to focus primarily on high and medium-weighted topics of the Registration Assessment Framework1, delivering interactive webinars where TPs answer exam-style questions with additional supplemental learning provided, where needed, by the expert tutor. Reference 1. General Pharmaceutical Council. Registration assessment framework for sittings in 2024. 2024. https://assets.pharmacyregulation.org/files/2024-04/registration-assessment-framework-for-sittings-in-2024.pdf [accessed 3rd June 2024]

The Effect of Job Interview Question Type on Faking and Interviewee Reactions

The Effect of Job Interview Question Type on Faking and Interviewee Reactions

What’s Your Problem? Writing Effective Research Questions for Quality Publications

Research questions play a central role in papers, indicating the phenomenon or problem that is being addressed, and clearly stating what is being investigated. Indeed, the presence of research questions within a research article is an established quality indicator, and research questions form the basis of determining hypotheses, methodology, methods, and how interpretations and analysis of outcomes are undertaken. Given the key purpose of a research article is to communicate the phenomenon or problem that is being addressed, research questions play a central role in clearly stating what is being investigated. The Journal of University Teaching and Learning Practice (JUTLP) seeks to publish high quality research papers which offer impactful guidance for teaching and learning practice in higher education. In this editorial, we explore what research questions are, how they are used, their key characteristics, different types of questions, and how to write them. We also make practical recommendations for authors, reviewers, and editors in how to use research questions, what to look out for when reviewing manuscripts and making decisions, and how to best support authors in producing quality publications by ensuring impactful research questions are included.

Enhancing Assessment Practices with Moodle: A Comprehensive Study on Teachers’ Use of Different Question Types and the Impact of FastTest PlugIn

This study investigates the evolution of online assessment practices through Moodle, a leading Learning Management System (LMS). It explores Moodle’s array of question types alongside the transformative impact of FastTest PlugIn (FTP) on educators’ assessment methods during and after the COVID-19 pandemic. This research examines the efficacy of various question types and import formats for large-scale question elaboration. Surveys have been conducted before and after developing courses to train educators on FTP. By analysing trends before and after adopting FTP, the study aims to gain insight into how educators from different universities and disciplines have adapted their assessment strategies among technological advancements. The introduction of FTP has significantly simplified question creation and integration within Moodle. The study also highlights differences in assessment practices between STEM and non-STEM disciplines. Surveys indicate that, following the FTP training course, the use of all question types has increased: Matching (35%), Missing Word (39%), and Cloze (22%). This work provides a comprehensive overview of current LMS trends and Moodle’s evolving role in educational technology. It emphasises the impact of technological advancements, such as FTP, on assessment practices and aims to offer actionable insights for enhancing online assessments.

Large language models (LLMs) in radiology exams for medical students: Performance and consequences.

The evolving field of medical education is being shaped by technological advancements, including the integration of Large Language Models (LLMs) like ChatGPT. These models could be invaluable resources for medical students, by simplifying complex concepts and enhancing interactive learning by providing personalized support. LLMs have shown impressive performance in professional examinations, even without specific domain training, making them particularly relevant in the medical field. This study aims to assess the performance of LLMs in radiology examinations for medical students, thereby shedding light on their current capabilities and implications.This study was conducted using 151 multiple-choice questions, which were used for radiology exams for medical students. The questions were categorized by type and topic and were then processed using OpenAI's GPT-3.5 and GPT- 4 via their API, or manually put into Perplexity AI with GPT-3.5 and Bing. LLM performance was evaluated overall, by question type and by topic.GPT-3.5 achieved a 67.6% overall accuracy on all 151 questions, while GPT-4 outperformed it significantly with an 88.1% overall accuracy (p<0.001). GPT-4 demonstrated superior performance in both lower-order and higher-order questions compared to GPT-3.5, Perplexity AI, and medical students, with GPT-4 particularly excelling in higher-order questions. All GPT models would have successfully passed the radiology exam for medical students at our university.In conclusion, our study highlights the potential of LLMs as accessible knowledge resources for medical students. GPT-4 performed well on lower-order as well as higher-order questions, making ChatGPT-4 a potentially very useful tool for reviewing radiology exam questions. Radiologists should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses. · ChatGPT demonstrated remarkable performance, achieving a passing grade on a radiology examination for medical students that did not include image questions.. · GPT-4 exhibits significantly improved performance compared to its predecessors GPT-3.5 and Perplexity AI with 88% of questions answered correctly.. · Radiologists as well as medical students should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses.. · Gotta J, Le Hong QA, Koch V et al. Large language models (LLMs) in radiology exams for medical students: Performance and consequences. Fortschr Röntgenstr 2024; DOI 10.1055/a-2437-2067.

A Guided Tour of Phylogenetic Comparative Methods for Studying Trait Evolution

Phylogenetic comparative methods are important tools in biology, providing insights into the way traits evolve. There are many technical resources describing how these methods work. Our aim here is to complement these with an overview of the types of biological questions that can be addressed by different methods and to outline potential pitfalls and considerations when embarking on comparative studies. First, we introduce what comparative methods are and why they are important. Second, we outline how they can be used to understand when, where, and how frequently traits evolve. Third, we examine how the coevolution of traits within and between species can be studied, along with patterns of causality. Finally, we discuss how to approach comparative analyses and the ways in which different types of data, such as published relationships, omic, and remote sensing data, can be integrated.

The use of concept maps in nursing/midwifery exam papers and their examination on their feasibility and reliability as assessment tools

BackgroundConcept maps demonstrate relationships between concepts visually, allowing connections to develop the learner's cognition. Despite studies that demonstrate the value of this method, it has still not been adopted in mainstream nursing education as an assessment tool. AimThe aim of this study is to evaluate the use of the concept map as an assessment tool in nursing education. MethodsThis study was conducted following the one-shot experimental design. One exam paper was designed to be given to participants at the final examination of a core shared undergraduate nursing/midwifery module. The target sample of the study were undergraduate first year students (N = 185). Following initial exposure of student nurses/midwives, during the Autumn semester, concept maps were included into the final exam. These were evaluated and compared with another of the most traditional assessment methods, the multiple-choice question type (MCQ). Quantitative statistical analysis of the concept map and MCQ grades were conducted. Statistical tests were used to compare all pairs of both types of papers. ResultsAll paired t-test results were found to be statistically significant and effect size results also supported the statistically significant results. Concept maps' structure allows the assessment of the totality of concepts of a specific area/section of the material. MCQs can assess one aspect/detail of a concept at a time without the limitation to draw material from a specific area/section. When used together, educators can use these different features in assessing student's knowledge/understanding on the totality of taught material. Additional advantage of the concept map is its straightforward, objective and quick correction process. ConclusionThe study provides evidence that the concept map can be a trusted assessment tool for identifying the nursing students' gaps in knowledge and comprehension.

Evidence-Based Potential of Generative Artificial Intelligence Large Language Models on Dental Avulsion: ChatGPT Versus Gemini.

In this study, the accuracy and comprehensiveness of the answers given to questions about dental avulsion by two artificial intelligence-based language models, ChatGPT and Gemini, were comparatively evaluated. Based on the guidelines of the International Society of Dental Traumatology, a total of 33 questions were prepared, including multiple-choice questions, binary questions, and open-ended questions as technical questions and patient questions about dental avulsion. They were directed to ChatGPT and Gemini. Responses were recorded and scored by four pediatric dentists. Statistical analyses, including ICC analysis, were performed to determine the agreement and accuracy of the responses. The significance level was set as p < 0.050. The mean score of the Gemini model was statistically significantly higher than the ChatGPT (p = 0.001). ChatGPT gave more correct answers to open-ended questions and T/F questions on dental avulsion; it showed the lowest accuracy in the MCQ section. There was no significant difference between the responses of the Gemini model to different types of questions on dental avulsion and the median scores (p = 0.088). ChatGPT and Gemini were analyzed with the Mann-Whitney U test without making a distinction between question types, and Gemini answers were found to be statistically significantly more accurate (p = 0.004). The Gemini and ChatGPT language models based on the IADT guideline for dental avulsion undoubtedly show promise. To guarantee the successful incorporation of LLMs into practice, it is imperative to conduct additional research, clinical validation, and improvements to the models.

Commercial forensic interviewing in the South African context: the Ponaletso CFI framework

Purpose The continued prevalence of financial crime and the lack of effective commercial forensic interviewing strategies prompted the development, presented here, of a conceptual framework to explain the relational dynamics conducive to interactions during a commercial forensic interview (CFI). The purpose of this study is to present such a framework which may facilitate the process of obtaining admissible evidence in different forums. Design/methodology/approach A grounded theory approach was adopted to develop a conceptual framework, drawing on the findings of a scoping review, direct observations of interactions involving commercial forensic practitioners (FPs) and interviewees, and data obtained from semi-structured interviews, making use of critical realism as a meta-theoretical framework. Findings Data obtained from the review informed the integrated approach of the conceptual framework, thus combining the structure of the PEACE model and creating an optimal interpersonal context informed by the person-centred approach (PCA). Observational data were used to identify the relational qualities associated with the interpersonal styles (clusters of relational qualities) of FPs in the CFI context, thereby contributing to complex relational dynamics. Interview data were used both to describe effective and ineffective interpersonal styles and how they played out in the CFI with interviewees, yielding information that unfortunately proved not to be admissible in a legal context. The findings of the different phases indicated the need for an integrated conceptual approach, the Ponaletso CFI framework, which draws on the structure of the PEACE model and the PCA, as expressed in the relational qualities of interactional pattern analysis (IPA). The framework consists of the interview stage (before, during and after), the structure (phases) of the PEACE model, the type of questions (open- or closed-ended) according to the PCA, FPs’ interpersonal styles (clusters of relational qualities as explained by IPA) and the observable relational dynamics between FPs and interviewees. When FPs use the Ponaletso CFI framework, there is a greater likelihood of extracting admissible evidence because the interview is guided by a clear structure with a step-by-step questioning approach by FPs, while taking into consideration the relational dynamics to which they contribute. Originality/value The proposed conceptual framework considers relational dynamics and how FPs can navigate towards optimal relational dynamics in CFI contexts. FPs can apply the structure, ask appropriate questions at the relevant phases and observe the relational dynamics playing out in the interpersonal context of the CFI. To the best of the authors’ knowledge, this is the first attempt at creating an integrated interviewing framework for conducting CFIs aligned with the legal context in South Africa. Using the Ponaletso CFI framework may aid FPs in obtaining admissible evidence and facilitate an optimal interpersonal context during commercial forensic investigations while respecting interviewees’ constitutional rights.