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With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

Background: Different types of assessments influence learning and learning behaviour. Multiple‐choice questions (MCQs) reward partial knowledge and encourage surface learning, while open‐ended questions (OEQs) promote deeper learning. Currently, MCQs is part of team‐based learning (TBL) curriculum, and it is challenging to implement OEQs as immediate feedback is necessary. Objectives: We asked if MCQ and OEQs affect student achievement, student learning and student engagement differently in a TBL classroom. Methods: MCQs and OEQs test scores of N = 66 students were automatically captured in Learning Activity Management System (LAMS) and were compared using a switching replications quasi‐experimental design with pre‐ and post‐tests to answer the research questions. Student learning approaches and engagement in the team activities were assessed using the study process questionnaire and the structure of observed learning outcomes taxonomy respectively. Results and Conclusions: Students get significantly higher MCQ scores than OEQs for the same set of questions, but the reverse is true for application exercises (AEs), which focus on higher‐level application. Most students significantly deepened their learning approaches before OEQs, while poorly prepared students were less engaged during OEQ discussions. Interestingly students subjected to OEQs took less time and scored higher in AE discussions, suggesting better focus on higher‐level thinking. Implications: This project is significant as it bridges our understanding of the value of OEQs and TBL. Our approach is transferable to other courses, and thus it can improve the quality of teaching and learning in tertiary education. Lay Description: What is already known about this topic?: Multiple‐choice questions (MCQs) do not promote deep learning as much as open‐ended questions (OEQ) do.Team‐based learning (TBL) requires MCQs because of the need for immediate feedback.Automated marking can make it more efficient to provide immediate feedback on OEQs as required by the TBL approach. What this paper adds?: OEQs changed student‐learning approaches in the TBL context.OEQs changed the quality of team discussions during the team‐readiness assessment.Test (tRAT) and application exercise phases.Students were concerned about the validity of automated grading. Implications for practitioners: Consider using OEQs in TBL context for deepening student learning.Consider how to defuse students' scepticism of new technology.Consider forming teams with a mix of deep and surface learners. [ABSTRACT FROM AUTHOR]