The Adaptive Lab Mentor (ALM): An AI-Driven IoT Framework for Real-Time Personalized Guidance in Hands-On Engineering Education

Abstract

Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant.

Keywords: IoT in education; adaptive learning; experiential learning; intelligent tutoring systems; sensors data analytics.

Figure 9

Receiver Operating Characteristic (ROC) curve for each circuit state class.

Figure 10

Precision–Recall (PR) curves for each circuit state class.

Figure 1

A comprehensive analysis of feature coverage across educational systems. The proposed Adaptive Lab Mentor (ALM) framework demonstrates significant advancements by providing comprehensive, integrated capabilities where existing systems offer only partial functionalities. (Scale: 0 = Minimal, 1 = Rule-Based, 2 = Partial, 3 = Full Implementation).

Figure 2

Architecture of the proposed Adaptive Lab Mentor (ALM) system. The principal element of this project is the Physical Sensor Layer. A Raspberry Pi is the second layer (Edge AI Layer), where real-time data fusion and inference are performed with a custom 1D-CNN model. The Application Layer gives the student adaptive feedback through a web interface.

Figure 3

(A) INITIALIZING: Gray status showing the system is establishing communication or awaiting the first stable reading. (B) SHORT CIRCUIT: Red critical fault status demanding immediate action to prevent damage by checking for shorted terminals. (C) CORRECT: Green status and encouraging message when the circuit matches the target resistance. (D) WRONG RESISTOR: Yellow warning and specific guidance to check component values.

Figure 4

Distribution of samples across the four circuit state classes.

Figure 5

Training and validation loss curve over 70 epochs.

Figure 6

Training and validation accuracy curve over 70 epochs.

Figure 7

Classification performance metrics (precision, recall, and F1 score) for each circuit state class.

Figure 8

Confusion Matrix.

Figure 11

t-SNE visualization of the learned feature latent space.