2025· International Journal of Science and Engineering· 0 citations
TL;DR
The idea of AI-resilient assignments maintaining academic integrity with product- and process-based evaluation approach is proposed, similar to the open-book system, where they require human intelligence, independent thinking, and personal understanding to solve them correctly.
Abstract
The emergence of AI in education has created both opportunities and challenges, especially in students’ examinations and assessments. Today, AI can solve almost any problem in seconds and provide answers in any style. This is useful in education, but its lead to misuse of AI in assignments and examinations, where students can solve the questions through AI, without independently thinking about the questions. AI provides simpler solutions, according to the prompt and can mimic human-like responses. Using AI to solve assignment questions has posed a challenge to the development of creative and critical thinking. Recently, students are directly copying AI-generated texts and pasting or writing in their answer sheets. Although AI has the potential to solve any problem, it has become a challenge for educators to evaluate ethically. There are several tools available, like plagiarism detection tools and AI-content detectors. This paper proposes the idea of AI-resilient assignments maintaining academic integrity with product- and process-based evaluation approach. These AI-resilient assignments are similar to the open-book system, where they require human intelligence, independent thinking, and personal understanding to solve them correctly.
This article re-examines the role of assessment within the rapidly evolving landscape of artificial intelligence (AI), focusing specifically on differentiated instruction, and highlights the potential of AI to not only streamline the assessment process but also cultivate a more equitable and student-centered learning environment.
H. Trinh· VNU Journal of Foreign Studi...· 0 citations
Given the rapid advancements in Artificial Intelligence (AI) tools, endless potential is reshaping how students learn, exceeding the boundaries of time, place and the variety of learning modalities. This study explores the ability of AI personas to replicate or refine the benefits of experiential learning, particularly in crisis-affected educational contexts. By conducting a literature review and qualitative analysis of student reflections and AI-generated dialogue, this research measures how effective the text-based AI personas, developed through carefully crafted prompts and outputs, can simulate real-world field interactions. The study centers on students participating in the Experiential Learning HEHI 303 course as part of the Certificate in Innovation Management in Contexts of Uncertainty at the American University of Beirut, who, as a result of the war outbreak in Lebanon, were unable to conduct fieldwork. In response, AI personas were introduced as alternatives for conducting virtual interviews and stakeholder engagement exercises. The findings imply that while AI personas can demonstrate a degree of emotional authenticity, cultural sensitivity, and contextual relevance, they also show limitations in fully capturing the spontaneity and unpredictability of humans. Instances of bias and lack of depth were observed in some responses. However, the study emphasizes the growing value of AI personas in educational settings especially when physical fieldwork is not feasible, while emphasizing the importance of prompt design and human oversight in AI-mediated learning.
A context is established in which reflective classroom surveys can help teachers implement process-based grading structures to empower students in their growth as authentic learners instead of defaulting to passive users of AI.
Erik N. Powel· Journal of Education and Lea...· 0 citations
This research explores the use of a latent Dirichlet allocation model to automatically classify students' design reflections, thereby improving the efficacy of their reviews, and advocates for professional engineering licensure bodies to modernize policies to encourage thoughtful, rigorous evaluation of AI models before deployment.
Brian Macdonald, Sister Libby Osgood, Christopher Power· Proceedings of the Canadian...· 0 citations
There is ongoing academic debate on whether one can teach AI literacy to undergraduate students across majors, and if yes, how. This article reports a case study: a three-week midterm project embedded in an undergraduate “AI-for-all” course. Students designed reasoning tasks, ran controlled comparisons across widely used chatbots, and evaluated both answer correctness and explanation validity. Through field experience, students with no STEM background learned what consumer chatbots can and cannot do, documenting systematic brittleness across models that “sounded right but reasoned wrong.” More critically, students built understanding of how to evaluate AI outputs. The midterm gave them agency as investigators rather than passive users. Eager to share their discoveries, they are co-authors of this article. Together, we offer here to educators and the broader scientific community a concrete example of the operationalization of AI literacy as experimental practice. The method, however, is not specific to the classroom. It shows any user how to test an AI system rather than trust it blindly. In three-week midterm project, students investigated whether AI literacy can be taught to undergrads.
Amarda Shehu, Adonyas Ababu, Asma Akbary et al.· Communications of the ACM· 0 citations
The emergence of Artificial Intelligence (AI) has had a profound impact on diverse sectors, including education. While the integration of technology into classrooms has been a constant for decades, both educators and students face new challenges due to the advent of NLP systems. Educators deal with analyzing and evaluating the originality of an essay. With the application of AI, students can answer theoretical questions and solve complex exercises, but grapple with the challenge of collaborating and engaging with a tool that relies on natural language instructions but can also provide outright incorrect responses. Our study aims to assess the effectiveness of ChatGPT in solving a case study with quantitative questions. We seek to compare the performance of students who use the tool with those who do not, from formulating instructions to validating or assessing the results. The data collected through pre-, and post-test questionnaires reveal consistently better results in students who did not use ChatGPT. However, the expectation of result accuracy and confidence in the outcomes obtained are similar in both groups. We suggest this to be interpreted as an effect that we call "Blind to Fraud" describing the situation in which students appear unaware of the likelihood of inaccuracy when using an AI-based NLP tool, which shows overconfidence in the results obtained. This can significantly impact learning outcomes and should be counterbalanced through critical thinking skills in education.
Emilio Velasco-Bartolomé, Sofía Ruiz-Campo· Revista Latinoamericana de T...· 0 citations
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