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Abstract Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system’s accuracy and performance. 

Despite providing many valuable ecosystem services, seagrasses are a threatened habitat and their global distribution is not fully known. For example, Venezuela lacks a national seagrass map. An established regional mapping approach for seagrass exists for the Google Earth Engine (GEE) platform, but requires a long time window to obtain sufficient data to overcome cloud and other challenges. Recently, GEE has released a Cloud Score+ quality band product for the purpose of cloud masking. Cloud masking could potentially reduce the time window needed for a representative multitemporal composite, which would allow for temporal analyses. We compare the performance of Cloud Score+ derived products against previously established multitemporal image composites acquired in different time ranges, and the ACOLITE‐processed single image composite. The Sentinel‐2 (S2) Level‐1C (L1C) imagery for the whole Venezuelan coastline was processed following three different approaches: (a) using a multitemporal composition of the full S2 L1C archive available and processed in GEE using the Dark Object Subtraction; (b) integrating Cloud Score+ data set into the previous approach; and (c) using a single‐image offline approach applying ACOLITE atmospheric correction. Additional raster features were generated and a two‐step classification approach was performed with five classes, namely sand, seagrass, turbid water, deep water, and coral, and bootstrapped 20 times. Quantitatively, the performance within the Cloud Score+ derived products were largely similar. While the full archive approach had the best quantitative results, the ACOLITE approach produced the best maps qualitatively. With this, we produced the first national seagrass map for Venezuela. 

Urbanization and population growth in coastal communities increase demands on local food and water sectors. Due to this, urban communities are reimagining stormwater pond infrastructure, asking whether the stormwater can be used to irrigate food and grow fish for local consumption. Studies exploring this feasibility are limited in the literature. Driven by a community’s desire to co-locate community gardens with stormwater pond spaces, this research monitored the water quality of a 23.4-hectare stormwater pond located in East Tampa, Florida over one year using the grab sample technique and compared the results with U.S. Environmental Protection Agency (EPA) reuse recommendations, EPA national recommended water quality criteria for aquatic life, and human health. pH and conductivity levels were acceptable for irrigating crops. Heavy metal (arsenic, cadmium, copper, lead, and zinc) concentrations were below the maximum recommended reuse levels (100, 10, 200, 5000 and 2000 µg/L, respectively), while zinc and lead were above the criteria for aquatic life (120 and 2.5 µg/L, respectively). E. coli concentrations ranged from 310 to greater than 200,000 MPN/100 mL, above the 0 CFU/100 mL irrigation requirements for raw food consumption and 200 CFU/100 mL requirements for commercial food processing. Synthetic organic compounds also exceeded criteria for human health.