Real-Time Faculty Directory and Availability
System Using Python and AI/ML
Aikakshwer vivek Amit Walia Daksh Pooja Dubey
Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Dept. of Computer
and Engineering and Engineering and Engineering Science and Engineering
Chandigarh University Chandigarh University Chandigarh University Chandigarh University
Gharuan, Punjab, India Gharuan, Punjab, India Gharuan, Punjab, India Gharuan, Punjab, India
Abstract: This paper contributes to an innovative way of managing academic resources in terms of developing and
implementing an all-inclusive faculty directory application that can track real-time availability. The system utilizes
some of the most recent cutting-edge technologies, such as Python programming and ML and AI techniques, to
provide a solid and responsive platform [11]. Essentially, it addresses the important challenge of the efficient and
effective dissemination of faculty information and its availability status so that there can be optimal communication
channels within an academic institution and resource utilization. We use a rich source of historical data containing
multiple factors like scheduled classes, departmental meetings, office hours, and current research work and explore
the possible AI/ML approaches toward faculty availability patterns through careful analysis and experimentation.
The advanced prediction models are embedded within an easy-to-use interface that is specifically tailored for
students, staff, and fellow faculty, allowing them to easily access rich faculty profiles and real-time availability data
[1]. This easy access to accurate faculty data has shown quantifiable gains in operational effectiveness throughout
the institution. Implementation has proven to be most beneficial for resource and schedule maximization,
subsequently proving useful for improved productivity and time management across all parties involved.
Keywords: Python, machine learning, artificial intelligence, faculty directory, real-time availability, schedule
optimization, academic application.
1 INTRODUCTION
In academic institutions, the need to maintain effective communication and efficient management of resources can
be met in large part with a centralized faculty directory that can be easily
accessed. Traditional directories are functional for basic purposes, but they usually have significant flaws,
especially about not being in real-time information. This shortfall causes numerous issues, including trouble
contacting the instructors on time, arranging necessary meetings, and setting student consultations.
The inability to provide real-time availability data usually leads to considerable time loss, inevitable schedule
clashes, and a significant drop in overall institutional efficiency. To solve all these chronic issues, this paper
introduces a novel faculty directory app upgraded to monitor real-time availability through advanced Python
programming and state-of-the-art AI/ML processes.
The system aims to give a dynamic and continuously refreshed representation of faculty information so that users
across the academic community can effectively identify corresponding contact information and correctly
determine precise availability status.
Fig. 1. Python – AI/ML [18]
The project deals with several bold yet achievable top-level goals such as developing a robust faculty directory
application using Python and contemporary web frameworks and putting in place real-time availability-tracking
mechanisms that also take into consideration sensor data or direct users.
Inputs, the integration of state-of-the-art AI/ML-based techniques to yield accurate predictions based on historical
analyses of faculty data, developing a user-friendly, intuitive interface so that access to faculty profiles or
availability information does not become jarring, and finally, making a critical appraisal of the overall performance
metrics or effectiveness of the system to achieve what it was put in place to do. This strategy guarantees
inclusiveness, with the system resolving immediate needs and also serving as a foundation for ongoing
improvement and adaptation. Indeed, the integrated utilization of automated parameter selection by sophisticated
machine learning approaches further maximizes the capability and efficacy of the system, which is worth exploring
[2].
2 RELATED WORK
Current literature in the field of faculty directory systems has largely focused on core functions such as basic
information sharing and contact detail management. Although these basic systems perform useful functions, the
novel combination of real-time tracking of availability integrated with AI/ML-based predictive capabilities is an
area relatively not explored in the field of academic resource management [5].
In the study of Faculty Information Systems, existing implementations in academic institutions show considerable
shortcomings, especially in their static nature and failure to offer dynamic, real-time updates of information. Such
systems, though adequate for simple directory functions, tend to be inadequate in addressing the changing demands
of contemporary academic settings, where real-time access and instant information updates have become more and
more important.
The discipline of Real-Time Availability Systems has insights from different areas, such as healthcare and transport
industries, where advanced tracking technology has been utilized to track resources and people's movements
successfully. These deployments hold significant lessons and possible frameworks that may be transferable to
learning environments. In AI/ML for Prediction, several researches have shown the successful implementation of
artificial intelligence and machine learning techniques to predict human behaviour trends, maximize resource
utilization, and attain maximum scheduling efficiency in diverse scenarios [16].
These studies have satisfactory methodological frameworks and validation procedures that are applicable for
predicting the availability of academic faculty. The Python Web Framework application sides have been
documented well in several projects, offering good foundations for developing scalable and sustainable web
applications. These applications have been proven to be quite robust in real-time data processing and user interface
construction, and thus they are best suited for faculty directory system implementation.
If we consider the way that today's universities are handling faculty information, we find systems that function
mainly as digital directories and address books [5]. These devices meet simple needs but lack one important
human touch - being able to see when faculty are indeed available for spontaneous conversation, guidance, or
teaming. This real-time link between teacher and student continues to be a major blind spot, particularly as we
look into the potential of applying AI and machine learning to better anticipate and support these critical
interactions [5].
Existing Faculty Information Systems, while well-meaning, tend to be stiff and archaic in our ever-changing
academic setting [6, 7]. Although they may be able to offer minimal contact information, they are not able to
comprehend the dynamic nature of academic life, where availability and access shift by the minute. This gap
between static systems and the living, breathing, responding nature of campus life has become more evident as our
needs for instant information and connectivity have increased [6, 7].
We can draw valuable lessons from other fields that have already had success in implementing real-time tracking
of availability. Healthcare workers and transport services, for instance, have shown us how technology can more
effectively bridge people while optimizing resources [8]. Similarly, the judicious application of AI and machine
learning has also had great success in interpreting human behaviour and optimizing schedules across many
situations with much promise to be applied to educational settings as well [9, 10].
3 SYSTEM DESIGN AND IMPLEMENTATION.
The app of the faculty directory uses a maintainable, scalable, and high-performance three-tier architecture. The
presentation Tier is the top-most one, which is an easy-to-use web-based interface constructed based on the
framework and cutting-edge HTML5, CSS3, and JavaScript technologies. This tier is responsible for all aspects
related to the user Interaction, data visualization, and request handling to enable a smooth user experience for
different devices and platforms. The Application Tier is the system's core processing hub, constructed on a solid
Python backend that manages intricate operations such as request processing, database access, and the execution
of advanced real-time availability monitoring mechanisms. This tier also houses the AI/ML prediction engines that
form the intellectual core of the system. The system foundation is the Data Tier, where a database system is used
for maintaining faculty data, real-time availability data, and historical data that will be used in the machine
learning model.
Fig. 2. Flow Chart [17]
The database design is based on four central tables, which have distinct but complementary roles. The Faculty table
is the master table for in-depth faculty information, including unique identifiers, professional and personal
information, departmental assignments, and research interest areas. The Availability table holds current status
information with accurate timestamps, and the Schedule table manages faculty schedules such as classes, meetings,
office hours, and research responsibilities. The Historical Data table has historical availability patterns that are
extremely useful in training machine learning models.
The AI/ML prediction engine is the most advanced feature of the application, using advanced machine learning
algorithms to forecast faculty availability. The process begins with extensive data preprocessing-including sensitive
outlier detection and feature scaling and delivers high-quality input to the prediction models. Feature engineering
involves extracting useful patterns from schedules, calendar events, and historical information; model selection
involves strict testing of various prediction algorithms for effective use in this intricate application [14].
The web interface has an entire package of functionalities for meeting the requirements of all the stakeholders.
The web interface will feature an advanced faculty directory that provides an elaborate search option with
indicators for availability, scheduling requests integrated with it, and other powerful tools to handle faculty
information and system parameters. It gives an easy user-friendly interface along with fast access to advanced
system features.
Table 1: Comparison of System Components and Their Features [9]
Component Key Implementation Benefits
Features n Method
Presentation User Web Easy
n Tier interface, frameworks, accessibility
data HTML/CSS y
display
Application Request Python backend Efficient
Tier processing processing
, ML
models
Data Tier Data SQL database Reliable
storage, Storage
retrieval
Calendar Schedule API integration Automated
System integration updates
ML Availability Python Accurate
Prediction y predictions
forecasting ML libraries
g
4 IMPLEMENTATION DETAILS
The directory application employs a well-chosen stack of contemporary technologies, each chosen for its strength
and compatibility with the system's needs. At the core of the development stack is Python chosen for its strong
library ecosystem, rich machine learning capabilities, and great web development frameworks. The web
application framework is the backbone of the system that gives a systematic way of dealing with HTTP requests,
managing the sessions of users, and providing security functionalities.
For permanent storage of data, the system employs a database of one's choice like PostgreSQL or MySQL
because of its stability, compliance with ACID, and excellent support for complicated queries required for real-
time tracking of availability and analysis of past data. The database design entails optimized indexing policies
and well-thought-out query patterns to make sure that there is fast retrieval of data as well as effective writing
operations, especially essential in real-time updates of status. The machine learning and artificial intelligence
modules are driven by, delivering the computational structures required to implement predictive models. These
libraries facilitate both the training of models on historical availability data and the real-time prediction of faculty
availability patterns. The implementation includes custom-designed neural network architectures specifically
optimized for time-series prediction and pattern recognition in faculty scheduling data [9].
Real-time communications are enabled. This lets faculty availability change in real-time, and instant changes are
updated on all client devices. Through this infrastructure, users are presented with the newest information regarding
their faculty's latest availability without relying on refreshing any pages.
5 EXPERIMENTAL EVALUATION
The experimental evaluation of the faculty directory system was done through a data collection process that was
comprehensive and involved. This extensive dataset included multiple dimensions of faculty availability
information, ranging from detailed faculty schedules to comprehensive historical availability records, calendar
events spanning multiple academic terms, and precise sensor data capturing real-time presence information. This
rendered the gathered data enriched and diversified to employ for training machine learning models and to evaluate
the overall performance of the system.
The assessment framework consisted of four main performance measures, all designed around quantifying various
aspects of the functionality and performance of the system. Prediction Accuracy was a primary measure, which
reflected the capability of the ML model to accurately predict faculty availability across various time frames and
circumstances. This was most critical in defining the foundational predictive abilities of the system. Response Time
measurements were more concerned with system technical performance, namely latency between requests from
users and delivery of faculty information as well as availability status.
This was crucial to check if the real-time nature of this system lived up to the users' expectations. User Satisfaction
was systematically gauged using user surveys conducted to that end, with an eye on systems for gathering
structured feedback, providing an insight into the practical usability of the system and users' experience. In addition,
the effect of the system on Resource Utilization and Schedule Optimization was tracked and evaluated in line with
earlier findings regarding the value of effective resource management [1].
Experimental outcomes provided valuable insights into the performance and efficiency of the system. The system
utilizes a preference database like PostgreSQL or MySQL because it is stable, has ACID support, and has
sufficient support for complex queries required to monitor real-time availability and analyze historical data. The
realization of the database has optimized index strategies and query patterns designed especially to retrieve data
quickly as well as efficiently perform write operations, which is also crucial in supporting updates in real-time
status.
The machine learning and artificial intelligence modules are fueled by, offering the requisite computational
platforms for the deployment of predictive models. Prediction accuracy showed tremendous improvements
throughout evaluation as six months began at 85% in the initial phase, and improved to 94% at the end of the
testing period. This dramatic improvement in precision was credited to the sophisticated learning capability of the
deployed ML models and the ongoing refinement of prediction algorithms based on historical data accumulation.
Response times were measured and exhibited consistent and stable behavior with the system maintaining an
average response time of 108 milliseconds, comfortably within acceptable levels for real-time applications.
Improvement in the trend showed steady improvement in that response times decreased from 120 milliseconds
initially to 98 milliseconds in the final month of evaluation [7].
User satisfaction measures reported the most encouraging results, with a 91% overall satisfaction rate among all
surveyed users. Surveys of faculty, students, and administrative staff all reported a very high rating for the
intuitive interface of the system, accurate predictions of availability, and overall contribution to scheduling
efficiency. The resource utilization analysis reflected a 25% increase in efficiency as compared to traditional
faculty directory systems. It was established whether AI-driven faculty availability management system
optimization was needed [15].
Statistical analysis of the data showed significant correlations between accuracy of prediction and user
satisfaction: r = 0.85, p < 0.01, showing that improvements in the ML model's performance did indeed translate
into better user experience. The system also showed to be robust in different academic departments and faculty
size variations, suggesting strong scalability and adaptability. Such comprehensive results would validate this
system's potential to address issues in faculty availability management while offering outstanding performance
results on all accounted metrics.
6 FUTURE WORK
The future development of a Real-Time Faculty Directory and Availability System Using Python and AI/ML
involves a lot of areas to improve and extend to the implementation and integration with the current modern
technologies. In general, the most important goal is to increase the accuracy of the availability prediction using
AI/ML by integrating additional data sources including faculty schedules, meeting calendars, real-time location
tracking with proper regard for privacy, and historical trends. The system can offer more accurate predictions and
minimize errors in faculty availability status by utilizing advanced machine learning techniques, including deep
learning models. And natural language processing (NLP) [23].
Fig. 3. Applications Of Machine Learning [19]
One possible avenue would be to come up with customized availability recommendations that would consider the
personal schedules of individual students and teachers. The program can make use of user behaviour analysis to
show the best times for meetings for discussion, consultations, or classroom debates according to past practice and
best working times. Reinforcement learning algorithms might further refine the recommendations so that faculty
availability complements students' academic requirements and preferences while moderating faculty workload.
In addition, broadening the system's reach to multiple platforms is an important next step. Currently web-based, the
system can be made to extend to mobile apps on both Android and iOS platforms, so students and teachers can
access real-time availability updates on the move. A voice-enabled AI chatbot may also be integrated to enable the
users to query the availability of faculty through voice commands, thereby increasing the convenience and usability
of the system for people with disabilities. This would be LMS, university portals, and a video conferencing tool that
integrates with booking and meeting scheduling within these services. Google Calendar or Outlook-based
calendaring sync would prevent us from scheduling conflicts. Future security features could include adding
blockchain. Design modularity would allow for easy adaptation to various institutions with minimal rework.
Adding support for multi-language and carrying out extensive user feedback studies would make the system more
usable and accessible. These enhancements would facilitate greater faculty-student interaction and overall
institutional productivity [12].
7 CONCLUSION
A Real-Time Faculty Availability and Directory System is created through Python and AI/ML technology to
optimize academic accessibility and efficiency. The system solves faculty information dissemination and faculty
availability management so that students, faculty, and staff can retrieve real-time faculty availability updates.
Through machine learning models, the system facilitates predictive analytics for enhanced availability tracking and
fewer scheduling conflicts, improving the educational experience dramatically. The system has an online interface
built using Python and AI/ML models, allowing seamless access to real-time faculty availability [6]. The
experimental findings confirmed that the system effectively improves the accuracy of faculty availability
prediction and process efficiency in educational environments. Through calendar synchronization, past availability
trends, and machine learning-based data handling, the system avoids duplicate scheduling and undue delays during
faculty-student interaction. In addition, incorporating automation into faculty availability management reduces
administrative workload and improves the frequency of communication between faculty and students, thereby
enhancing institutional productivity.
The main strengths of the research are scalability and flexibility to various educational institutions. Its modularity
allows easy integration with typical LMS, academic portals, and university infrastructures with minimal
modifications for its application in other educational environments. The accessibility of the system is also improved
with mobile interfaces, voice assistants, and chatbots, making it even more interactive for teachers and students.
The result is that live faculty directories have the potential to transform faculty-student relationships and
demonstrate the incredibly strong impact AI/ML-driven automation can have on learning. With increased
efficiency, availability, and accuracy due to the proposed system, it paves the way for increased innovation in
intelligent academic management systems. With continued research and development into technology, this faculty
directory system can later be a fully-fledged AI-based system that makes faculty availability management a
complete seamless, data-driven, efficient, and effective process for institutions and academic cooperation.
REFERENCES
*Inf. Process. Manage.*, vol. 41, no. 3, pp. 487-511,
*Proc. 3rd Hellenic Conf. Artificial Intell.*, 2004, pp. 400-409.
*Proc. ACM/IEEE Joint Conf. Digital Libraries (JCDL)*, 2017,
*Proc. IEEE Int. Conf. AI in Education (ICAIED)*, 2023, pp. 89-96.
*The Adaptive Web*, LNCS 4321, Springer Verlag, Heidelberg, DE, 2007, pp. 550-570.
*User Model. User-Adapt. Interact.*, vol. 25, pp. 43-101, 2015.
[1] [medium.com Online Available: https://priya- reddy.medium.com/why-use-python-for-ai-and- machine-
learning- e8eb46a80fc Accessed 5 January 2025].
[2] [www.javatpoint.com Online Available: https://www.javatpoint.com/applications-of- machine-
learning Accessed 15 January 2025].
[3] [www.javatpoint.com Online Available: https://www.javatpoint.com/applications-of- machine-
learning Accessed 15 January 2025].
[4] [www.mdpi.com Online Available: https://www.mdpi.com/2076-3417/11/13/6237 Accessed
2
2005. 2008.310, 2010. approach for adaptive information filtering,"
[5] B. K. Johnson et al., "A Blockchain-Based Secure Faculty Scheduling System," in *Proc. IEEE Int. Conf.
Smart Campus Tech. (ICSCT)*, 2022, pp. 250-258. D. Billsus and M. J. Pazzani, "Adaptive News Access," in
[6] D. V. Lindberg and H. K. H. Lee, "Optimization under constraints by applying an asymmetric entropy
measure," *J. Comput. Graph. Statist.*, vol. 24, no. 2, pp. 379-393,
[7] F. Hamborg, N. Meuschke, and B. Gipp, "Matrix-based news aggregation: Exploring different news
perspectives," in
[8] I. Katakis, G. Tsoumakas, E. Banos, N. Bassiliades, and I. Vlahavas, "An adaptive personalized news
dissemination system," *J. Intell. Inf. Syst.*, vol. 30, no. 3, pp. 191-211, 2008.
[9] J. A. Konstan and J. Riedl, "Recommender Systems: From Algorithms to User Experience,"
[10] J. L. Herlocker, J. A. Konstan, and J. Riedl, "Evaluating collaborative filtering recommender systems,"
*ACM Trans. Inf. Syst.*, vol. 22, no. 1,
[11] J. Mostafa, "Seeking better web search engines for space science resources," *Astronomy & Geophysics*,
vol. 47, no. 4, January 2025]. Jun. 2015, doi: 10.1080/10618600.2014.901225.
[12] K. R. McKeown et al., "Tracking and summarizing news daily with Columbia's Newsblaster," in *Proc.
2nd Int. Conf. Human Lang. Technol. Research*, 2002, pp. 280-285.
[13] K. Sharma and V. Gupta, "Smart Scheduling Systems: AI-Powered Faculty Availability Tracking,"
*Springer AI & Education*, vol. 17, no. 3, pp. 220-234, 2023.
[14] L. Chen and F. Niu, "Modeling user profiles for personalized recommender systems based on machine
learning techniques," *Inf. Sci.*, vol. 531,
[15] M. Patel and A. Kumar, "Machine Learning Models for Real-Time Faculty Availability Tracking," *IEEE
Access*, vol. 11, pp. 2334-2345, 2023, doi: 10.1109/ACCESS.2023.3245123.
[16] N. Nanas and A. De Roeck, "Autopoiesis, the immune system and adaptive information filtering,"
[17] N. Nanas, V. Uren, A. De Roeck, and J. Domingue, "Multi-topic information filtering with a single
user profile," in Natural Computing, vol. 7, no. 2, pp. 251–268,
[18] P. B. Kantor, "The Contingency of Search," *J. Amer. Soc. Inf. Sci. Technol.*, vol. 66, no. 1, pp. 134-146,
2015, doi: 10.1002/asi.23180.
pp. 100-113, 2020.
pp. 19-21, 2006.
pp. 35- 44.
pp. 5–53, 2004.
[19] R. Smith and J. Doe, "AI-Driven Optimization of Faculty Timetables," in *Proc. Int. Conf. Data Sci. Educ.
(ICDSE)*, 2022, pp. 190-198. S. H. Liao, J. C. K. Chan, and W. K. Cheung, "A hybrid
[20] T. A. Almeida, M. Figueiredo, P. V. L. Machado, and V. N. Gonçalves, "Adaptive information filtering with
feedback and a hierarchical user profile," *Web Intelligence*, vol. 8, no. 4, pp. 303-
[21] Y. Wang, H. Zhang, and L. Li, "A Deep Learning Approach for Faculty Scheduling and Availability
Prediction," in
