Augmented Creativity: Leveraging Natural Language Processing for Creative Writing

Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality. One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using “machine-in-the-loop” strategies in creative writing. In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education. An NLP application was created using Python and spaCy and deployed via Streamlit. The AI allowed students to see if their grammar aligned with those principles and techniques taught in class to assist with a deeper understanding of the grammatical aspects of the content and also to improve their creativity as writers. The study at hand seeks to determine the efficacy of a new proprietary NLP on improving understanding of grammar and creativity in student writing. Participants in the study were assessed through surveys and open-ended questions. Findings note that participants agreed the algorithm assisted them in a better understanding of grammar but were not as receptive to assistance in improving their creativity. It should also be noted that the suggestions provided by the algorithm did not necessarily improve the written artifacts submitted in the study. Results indicate that students enjoy using NLP as part of the creative writing process but largely, as with other language processing tools, to assist with grammar and synta

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence AI Generative AI like ChatGPT and Stable Diffusion along with a host of others launched late in the year and immediately disrupted the status quo of the literary and artworlds leading to outcries to ban AI Art and spawning an entirely new market of NFTs Fears over the death of the artist and the death of college composition however are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with poststructuralism and the Foucauldian Death of the Author in 196

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence (AI). Generative AI like ChatGPT and Stable Diffusion, along with a host of others, launched late in the year and immediately disrupted the status quo of the literary and art worlds, leading to outcries to ban “AI Art” and spawning an entirely new market of NFTs. Fears over the “death of the artist” and the “death of college composition,” however, are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with post structuralism and the Foucauldian Death of the Author in 1967. Contemporary scholarship has faced challenges in reconciling the function of the human author in conjunction with artificial intelligence (AI) due to the progressive sophistication and selfsufficiency of generative code. Nonetheless, it is erroneous to establish the threshold for authorship based on the development or advancement of AI or robotics, as it falls within the realm of ontology. Instead, assertions of AI authorship stem from a romanticized perception of both authorship and AI during a period in which neither holds significance. A new discussion on the role of the human agent in the writing process, particularly in the creative process like poetry, should prioritize the practical aspects of what an author does. This study examines how AI is increasingly becoming involved in collaborative efforts to create poetry and aims to explore the potential of this trend. Furthermore, the study seeks to provide empirical evidence on the boundaries of AI\u27s ability to replicate human thought and experience. Through generating content in the creative written arts using ChatGPT-3, poetry analysis revealed that, in fact, such new generative models can imitate the vocabulary, language choices, style, and even rhythm of famous poets such as Keats, it is unable to generate emotions that it has not experienced. The questions that will continue to be raised on the nature of humanity, existence, and creative capabilities should be reframed with the concept of fear fore grounded to assist in understanding the uniquely human anxiety and drive to create in an attempt to communicate across the gulf what it “feels” like to be human as a phenomenology of experience

New Trends in Second Language Learning and Teaching through the lens of ICT, Networked Learning, and Artificial Intelligence

In the last few decades, Information and Communications Technology (ICT) applications have been shaping the field of Computer Assisted Language Learning (CALL). Mobile Assisted Language Learning (MALL) paved the way for ubiquitous learning. The advent of new technologies in the early 21st century also added a social dimension to ICT that allowed for Networked Learning (NL). Given that language learning is fundamentally a socio-cultural experience, networked learning capabilities have provided the potential for language learning in community settings. This has revitalized the earlier frameworks provided by CALL. NL has empowered language learners today to connect globally, to access Open Educational Resources, and to self-regulate their learning processes beyond the scope of traditional curricula. In parallel, the rising pervasiveness of Artificial Intelligence (AI) applications and their relevance to language learning has led CALL to branch out into Intelligent CALL (ICALL). The first section of this article provides a brief historical overview of CALL, examines it through the lens of ICT, networked learning, and open access. The second section focuses on the implications of AI for creating new trends in second language education, the challenge for providing customization at scale, and raises important issues related to transparency and privacy for future research

Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement.  Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education

Working With (Not Against) the Technology: GPT3 and Artificial Intelligence (AI) in College Composition

The use of artificial intelligence (AI) for improvement of writing is commonplace with word-processing software and cloudbased writing assistants such as Grammarly and Microsoft Word. However, more and more options are cropping up that move beyond assistance with grammar, spelling, and punctuation to complete essay generation. The free availability of AI essay generators has led to lamenting the coming death of college writing. But AI has been used in the previously noted examples for decades without such a reaction. In fact, the idea that the use of essay generating software is synonymous with academic dishonesty is as passé as worries about allowing students to use calculators or chalkboards. Both are tools that emerged by affording students a different type of learning which was not rote memorization. The questions now become how AI tools can and should be used to teach English composition and to what extent. In the conceptual age where AI is used to augment all other facets of human creativity, providing students with the tools they will need for effective communication becomes inevitable. These new AI tools may allow students to master grammar and syntax more quickly in order to move on to important research questions that will contribute to knowledge in their given fields. This study investigates the current and potential uses of AWE, AAG and AI essay generators in a first-semester English composition classroom. Students in the study were provided with the same assignments and learning outcomes as are standard in English, composition courses but were encouraged to use AI applications when prompted to discover the usefulness and limitations of such technology. Results from the study confirm that use of such tools does not automatically lead to plagiarism or academic dishonesty. On the contrary, higher-order thinking skills and metacognition are required to use AI tools appropriately to learn writing skills. Furthermore, the tools themselves became the topic covered in the class for the study and led to further social and ethical implications

Language teachers’ perceptions on the use of OER language processing technologies in MALL

Combined with the ubiquity and constant connectivity of mobile devices, and with innovative approaches such as Data-Driven Learning (DDL), Natural Language Processing Technologies (NLPTs) as Open Educational Resources (OERs) could become a powerful tool for language learning as they promote individual and personalized learning. Using a questionnaire that was answered by language teachers (n= 230) in Spain and the UK, this research explores the extent to which OER NLPTs are currently known and used in adult foreign language learning. Our results suggest that teachers´ familiarity and use of OER NLPTs are very low. Although online dictionaries, collocation dictionaries and spell checkers are widely known, NLPTs appear to be generally underused in foreign language teaching. It was found that teachers prefer computer-based environments over mobile devices such as smartphones and tablets and that teachers´ qualification determines their familiarity with a wider range of OER NLPTs. This research offers insight into future applications of Language Processing Technologies as OERs in language learning.TELL-OP -Transforming European Learner Language into Learning Opportunities KA200 Higher Education Strategic Partnership 2014-1-ES01-KA203-004782 ERASMUS

Automated scoring of writing

For decades, automated essay scoring (AES) has operated behind the scenes of major standardized writing assessments to provide summative scores of students’ writing proficiency (Dikli in J Technol Learn Assess 5(1), 2006). Today, AES systems are increasingly used in low-stakes assessment contexts and as a component of instructional tools in writing classrooms. Despite substantial debate regarding their use, including concerns about writing construct representation (Condon in Assess Writ 18:100–108, 2013; Deane in Assess Writ 18:7–24, 2013), AES has attracted the attention of school administrators, educators, testing companies, and researchers and is now commonly used in an attempt to reduce human efforts and improve consistency issues in assessing writing (Ramesh and Sanampudi in Artif Intell Rev 55:2495–2527, 2021). This chapter introduces the affordances and constraints of AES for writing assessment, surveys research on AES effectiveness in classroom practice, and emphasizes implications for writing theory and practice.Englis

Deep Learning for Opinion Mining and Topic Classification of Course Reviews

Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course reviews publicly available online. We applied machine learning techniques with the goal to gain insight into student sentiments and topics. Specifically, we utilized current Natural Language Processing (NLP) techniques, such as word embeddings and deep neural networks, and state-of-the-art BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly optimized BERT approach) and XLNet (Generalized Auto-regression Pre-training). We performed extensive experimentation to compare these techniques versus traditional approaches. This comparative study demonstrates how to apply modern machine learning approaches for sentiment polarity extraction and topic-based classification utilizing course feedback. For sentiment polarity, the top model was RoBERTa with 95.5% accuracy and 84.7% F1-macro, while for topic classification, an SVM (Support Vector Machine) was the top classifier with 79.8% accuracy and 80.6% F1-macro. We also provided an in-depth exploration of the effect of certain hyperparameters on the model performance and discussed our observations. These findings can be used by institutions and course providers as a guide for analyzing their own course feedback using NLP models towards self-evaluation and improvement.Comment: Accepted and Published in Education and Information Technologies (Accepted March 2023