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Detecting generative artificial intelligence essays using large language models: Machine and deep learning approaches
(2024-05) Tariq, Rasikh; Casillas Muñoz, Fidel Antonio Guadalupe; Waqar, Muhammad Ashraf; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/02jx3x895; IEEE
The study focuses on discerning between human and AI-generated essays, highlighting the ethical implications of AI in academia. It employs various algorithms like logistic regression, Support Vector Machine (SVM), decision trees, random forests, KNN, and LSTM to develop models for essay classification. The TF-IDF technique (Term Frequency-Inverse Document Frequency) is applied to assess document word importance, with rigorous parameter tuning ensuring model accuracy. Findings revealed SVM's exceptional precision and recall, highlighting its robustness in accurately classifying essays, while decision trees offer simplicity but increased misclassification risk. KNN strikes a balance and random forests as well. LSTM excels in contextual understanding, albeit with higher computational demands. The research emphasizes the significance of algorithm selection in maintaining academic integrity and fostering genuine student creativity. SVM emerges as a robust and accurate choice for essay classification, ensuring fair assessment and upholding academic honesty.
Use of multimodal data value chain as a contribution to the management of the teaching-learning process in higher education institutions
(2021-12-15) Ruiz Ramírez, Jessica Alejandra; Glasserman Morales, Leonardo David; Instituto Tecnológico y de Estudios Superiores de Monterrey; Tecnológico de Monterrey
In education, data collection from students and teachers has occurred in physical spaces and, recently, more frequently in digital spaces. For this reason, the interaction of students and technologies offers an opportunity for multimodal data collection. We present the initial conceptual model of the Multimodal Data Value Chain (M-DVC). It clearly extracts and systematically specifies the raw evidence of learning required for a multimodal learning analytics solution (MMLA) that processes the data and converts it into meaningful information. We followed an educational action research methodology that integrated the researcher-educators into a collective process of producing and reproducing the knowledge necessary to transform the digital post-pandemic educational environment. The qualitative analysis of the MDVC conceptual model's processes made it possible to recognize the institution's characteristics. The analyses occurred in the macro (institution), meso (training program), and micro (subjects) contexts. The results defined the characteristics expected to be crucial for pedagogical decision-making based on results and reliable sources.

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