I’ll be honest, I haven’t been diving into AI software as fast as I should be. I continue to integrate ChatGPT into my software, but I haven’t explored the wider array of offerings by OpenAI. Recently, I was introduced to DALL·E from a student using it to create images for a capstone project. I did some more research to learn more about the software. DALL·E is an artificial intelligence model developed by OpenAI and builds upon the GPT (Generative Pre-trained Transformer) architecture, the same underlying technology as ChatGPT, but with a focus on generating images from textual descriptions. Its name “DALL·E” is a combination of “Dali” (after the surrealist artist Salvador Dalí) and “WALL·E” (the Disney-Pixar robot character), reflecting its ability to generate surreal and creative images from textual descriptions.

Here’s a more detailed explanation of DALL·E:

1. Model Architecture: DALL·E is based on a variant of the Transformer architecture, which is a neural network architecture originally designed for natural language processing tasks. It extends this architecture to perform image generation from text.

2. Text-to-Image Generation: DALL·E takes textual descriptions as input and generates corresponding images as output. The text input can be a short phrase, sentence, or even a longer paragraph, describing a particular scene or concept. The model’s primary goal is to generate images that match the textual description provided.

3. Creativity and Imagination: One of the interesting aspects of DALL·E is its ability to generate highly imaginative and surreal images. It can combine concepts and ideas in novel ways, creating visuals that may not exist in the real world. For example, if you describe “an armchair in the shape of an avocado,” DALL·E can generate a realistic image of just that.

4. Image Resolution: DALL·E can generate images at a resolution of 256×256 pixels, which, while not as high as some other image generation models, is still quite impressive given its ability to generate novel and creative visuals.

5. Training Data: DALL·E was trained on a massive dataset of text and images from the internet. This dataset includes various sources, allowing the model to learn from a wide range of concepts and ideas.

6. Ethical and Societal Considerations: DALL·E, like other AI models, raises ethical and societal considerations. The generated content can be used for both positive and negative purposes, and it’s essential to use the technology responsibly and consider its potential impact on privacy, copyright, and misinformation.

7. Limitations: DALL·E, like all AI models, has limitations. It may not always generate images that precisely match the textual descriptions, and the output can sometimes be unpredictable. Additionally, there is a possibility of the model generating inappropriate or harmful content.

8. Applications: DALL·E has a wide range of potential applications, including creative design, art, content generation, and even assisting in brainstorming and ideation processes. It can be a valuable tool for artists, designers, and anyone looking to visually represent their ideas.

As taken from ChatGPT, “DALL·E is intriguing for generating unique images for model that can generate images from textual descriptions, showcasing the capabilities of AI in the creative domain. Its ability to generate imaginative and surreal images has the potential to revolutionize various industries and creative processes. However, its use also comes with ethical considerations that need to be carefully addressed.”

At the same time, when asking DALL·E to provide me with a unique image that incorporates social media, artificial intelligence and WordPress, the following images was constructed:

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My colleagues and I recently published our article, “Formation and Action of a Learning Community with Collaborative Learning Software,” in the Journal of Management Information Systems. Below is the abstract and citation.

Abstract
This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model.

Reference
Evren Eryilmaz, Brian Thoms, Zafor Ahmed & Howard Lee (2023) Formation and Action of a Learning Community with Collaborative Learning Software, Journal of Management Information Systems, 40:1, 38-55, DOI: 10.1080/07421222.2023.2172774

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My colleagues and I recently presented and published research at EDULEARN 2022 titled, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”. The paper presents on differences in online interaction before and during COVID-19 lockdowns with an emphasis on sentiment analysis. Below is the tag cloud, abstract and full citation of the research.

Abstract
In early 2020, there was a drastic shift in the teaching modality across institutions of higher education as colleges and universities adapted to federal guidelines in the face of COVID-19 restrictions. While most, if not all institutions already had mature learning management systems (LMS), alternative approaches using social components proved invaluable during a time when face-to-face social interaction was largely restricted. More so, a large concern in the shift to remote instruction during COVID-19 lockdowns was the toll on student well-being as traditional learning management systems typically lack social networking capabilities. In this paper, we explore the use of online social networking (OSN) software to facilitate more positive interactions. More specifically, we analyse the sentiment within online discussions to show key differences between users using traditional LMS software and those using modified OSN software. Findings show that students participating during the pandemic, yielded higher levels of social presence and posted more positively to the discussion.

Reference
Thoms, E. Eryilmaz, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”, 14th Annual International Conference on Education and New Learning Technologies (EDULEARN 22), July 5, 2022.

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The following abstract is from a student co-authored paper, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” presented by Dr. Jason Isaacs at the 8th International Conference on Data Mining and Applications (DMA 2022), presented on May 28, 2022.

Abstract
Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.

Reference
M. Shariff, B. Thoms, J. Isaacs, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” Accepted for inclusion in 8th International Conference on Data Mining and Applications (DMA 2022), May 28-29, 2022.

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My colleagues and I recently published in the prestigious Journal of Management Information Systems (JMIS) titled, “Promoting learning community formation using recommender systems and learning analytics within a computer-supported collaborative learning software.” The paper provides an interesting deep dive into how collaborative recommendation systems facilitate knowledge communities. Below is the tag cloud, abstract, and full citation to the research.

Abstract
This paper explores the formation of a learning community facilitated by a specialized computer-supported collaborative learning asynchronous online discussion (AOD) tool. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed methods field study involving an AOD consisting of 259 messages posted by 50 participants. We applied degree and closeness centrality to cluster participants into three clusters corresponding to the central, intermediate, and peripheral community layers. The findings show that the intermediate cluster had the opportunity to influence others’ thinking within discourse, but it was the central cluster, which re-constructed the raw ideas affected by others’ feedback. The results further provide evidence that message lexical complexity does not correlate to the stages of knowledge building. Finally, results offer an initial insight into which ideas reached to higher stages of knowledge building among the clusters. Theoretical and practical implications are discussed.

Reference
E. Eryilmaz, B. Thoms, Z. Ahmed, KH Lee. “Promoting learning community formation using recommender systems and learning analytics within a computer-supported collaborative learning,” Journal of Management Information Systems, April 2022. (Impact Factor 7.84)

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